The initial commit for generic optimization

Generic optimization package for openCV project, will be developed
between the June and September of 2013. This work is funded by Google
Summer of Code 2013 project. This project is about
implementing several algorithms, that will find global maxima/minima of a
given function on a given domain subject to a given constraints.

All comments/suggestions are warmly appreciated and to be sent to
alozz1991@gmail.com (please, mention the word "openCV" in topic of
message, for I'm using the spam-filters)

modules/optim/CMakeLists.txt

+set(the_description "Computational Photography")
+ocv_define_module(photo opencv_imgproc)

modules/optim/doc/denoising.rst

+Denoising
+==========
+
+.. highlight:: cpp
+
+fastNlMeansDenoising
+--------------------
+Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/
+with several computational optimizations. Noise expected to be a gaussian white noise
+
+.. ocv:function:: void fastNlMeansDenoising( InputArray src, OutputArray dst, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
+
+    :param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
+
+    :param dst: Output image with the same size and type as  ``src`` .
+
+    :param templateWindowSize: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+    :param searchWindowSize: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
+
+    :param h: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
+
+This function expected to be applied to grayscale images. For colored images look at ``fastNlMeansDenoisingColored``.
+Advanced usage of this functions can be manual denoising of colored image in different colorspaces.
+Such approach is used in ``fastNlMeansDenoisingColored`` by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
+
+fastNlMeansDenoisingColored
+---------------------------
+Modification of ``fastNlMeansDenoising`` function for colored images
+
+.. ocv:function:: void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
+
+    :param src: Input 8-bit 3-channel image.
+
+    :param dst: Output image with the same size and type as  ``src`` .
+
+    :param templateWindowSize: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+    :param searchWindowSize: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
+
+    :param h: Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
+
+    :param hForColorComponents: The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors
+
+The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``fastNlMeansDenoising`` function.
+
+fastNlMeansDenoisingMulti
+-------------------------
+Modification of ``fastNlMeansDenoising`` function for images sequence where consequtive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces.
+For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394
+
+.. ocv:function:: void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
+
+    :param srcImgs: Input 8-bit 1-channel, 2-channel or 3-channel images sequence. All images should have the same type and size.
+
+    :param imgToDenoiseIndex: Target image to denoise index in ``srcImgs`` sequence
+
+    :param temporalWindowSize: Number of surrounding images to use for target image denoising. Should be odd. Images from ``imgToDenoiseIndex - temporalWindowSize / 2`` to ``imgToDenoiseIndex - temporalWindowSize / 2`` from ``srcImgs`` will be used to denoise ``srcImgs[imgToDenoiseIndex]`` image.
+
+    :param dst: Output image with the same size and type as ``srcImgs`` images.
+
+    :param templateWindowSize: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+    :param searchWindowSize: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
+
+    :param h: Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
+
+fastNlMeansDenoisingColoredMulti
+--------------------------------
+Modification of ``fastNlMeansDenoisingMulti`` function for colored images sequences
+
+.. ocv:function:: void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
+
+    :param srcImgs: Input 8-bit 3-channel images sequence. All images should have the same type and size.
+
+    :param imgToDenoiseIndex: Target image to denoise index in ``srcImgs`` sequence
+
+    :param temporalWindowSize: Number of surrounding images to use for target image denoising. Should be odd. Images from ``imgToDenoiseIndex - temporalWindowSize / 2`` to ``imgToDenoiseIndex - temporalWindowSize / 2`` from ``srcImgs`` will be used to denoise ``srcImgs[imgToDenoiseIndex]`` image.
+
+    :param dst: Output image with the same size and type as ``srcImgs`` images.
+
+    :param templateWindowSize: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+    :param searchWindowSize: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
+
+    :param h: Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
+
+    :param hForColorComponents: The same as h but for color components.
+
+The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``fastNlMeansDenoisingMulti`` function.
+

modules/optim/doc/inpainting.rst

+Inpainting
+==========
+
+.. highlight:: cpp
+
+inpaint
+-----------
+Restores the selected region in an image using the region neighborhood.
+
+.. ocv:function:: void inpaint( InputArray src, InputArray inpaintMask, OutputArray dst, double inpaintRadius, int flags )
+
+.. ocv:pyfunction:: cv2.inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dst
+
+.. ocv:cfunction:: void cvInpaint( const CvArr* src, const CvArr* inpaint_mask, CvArr* dst, double inpaintRange, int flags )
+
+    :param src: Input 8-bit 1-channel or 3-channel image.
+
+    :param inpaintMask: Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that needs to be inpainted.
+
+    :param dst: Output image with the same size and type as  ``src`` .
+
+    :param inpaintRadius: Radius of a circular neighborhood of each point inpainted that is considered by the algorithm.
+
+    :param flags: Inpainting method that could be one of the following:
+
+            * **INPAINT_NS**     Navier-Stokes based method.
+
+            * **INPAINT_TELEA**     Method by Alexandru Telea  [Telea04]_.
+
+The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video. See
+http://en.wikipedia.org/wiki/Inpainting
+for more details.

modules/optim/doc/photo.rst

+********************************
+photo. Computational Photography
+********************************
+
+.. highlight:: cpp
+
+.. toctree::
+    :maxdepth: 2
+
+    inpainting
+    denoising

modules/optim/include/opencv2/photo.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_PHOTO_HPP__
+#define __OPENCV_PHOTO_HPP__
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+
+/*! \namespace cv
+ Namespace where all the C++ OpenCV functionality resides
+ */
+namespace cv
+{
+
+//! the inpainting algorithm
+enum
+{
+    INPAINT_NS    = 0, // Navier-Stokes algorithm
+    INPAINT_TELEA = 1 // A. Telea algorithm
+};
+
+//! restores the damaged image areas using one of the available intpainting algorithms
+CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
+                           OutputArray dst, double inpaintRadius, int flags );
+
+
+CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
+                                        int templateWindowSize = 7, int searchWindowSize = 21);
+
+CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
+                                               float h = 3, float hColor = 3,
+                                               int templateWindowSize = 7, int searchWindowSize = 21);
+
+CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+                                             int imgToDenoiseIndex, int temporalWindowSize,
+                                             float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
+
+CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+                                                    int imgToDenoiseIndex, int temporalWindowSize,
+                                                    float h = 3, float hColor = 3,
+                                                    int templateWindowSize = 7, int searchWindowSize = 21);
+
+} // cv
+
+#endif

modules/optim/include/opencv2/photo/photo.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/photo.hpp"
\ No newline at end of file

modules/optim/include/opencv2/photo/photo_c.h

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_PHOTO_C_H__
+#define __OPENCV_PHOTO_C_H__
+
+#include "opencv2/core/core_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* Inpainting algorithms */
+enum
+{
+    CV_INPAINT_NS      =0,
+    CV_INPAINT_TELEA   =1
+};
+
+
+/* Inpaints the selected region in the image */
+CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask,
+                       CvArr* dst, double inpaintRange, int flags );
+
+
+#ifdef __cplusplus
+} //extern "C"
+#endif
+
+#endif //__OPENCV_PHOTO_C_H__

modules/optim/perf/perf_inpaint.cpp

+#include "perf_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+CV_ENUM(InpaintingMethod, INPAINT_NS, INPAINT_TELEA)
+typedef std::tr1::tuple<Size, InpaintingMethod> InpaintArea_InpaintingMethod_t;
+typedef perf::TestBaseWithParam<InpaintArea_InpaintingMethod_t> InpaintArea_InpaintingMethod;
+
+
+PERF_TEST_P(InpaintArea_InpaintingMethod, inpaint,
+            testing::Combine(
+                testing::Values(::perf::szSmall24, ::perf::szSmall32, ::perf::szSmall64),
+                InpaintingMethod::all()
+                )
+            )
+{
+    Mat src = imread(getDataPath("gpu/hog/road.png"));
+
+    Size sz = get<0>(GetParam());
+    int inpaintingMethod = get<1>(GetParam());
+
+    Mat mask(src.size(), CV_8UC1, Scalar(0));
+    Mat result(src.size(), src.type());
+
+    Rect inpaintArea(src.cols/3, src.rows/3, sz.width, sz.height);
+    mask(inpaintArea).setTo(255);
+
+    declare.in(src, mask).out(result).time(120);
+
+    TEST_CYCLE() inpaint(src, mask, result, 10.0, inpaintingMethod);
+
+    Mat inpaintedArea = result(inpaintArea);
+    SANITY_CHECK(inpaintedArea);
+}

modules/optim/perf/perf_main.cpp

+#include "perf_precomp.hpp"
+
+CV_PERF_TEST_MAIN(photo)

modules/optim/perf/perf_precomp.cpp

+#include "perf_precomp.hpp"

modules/optim/perf/perf_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_PERF_PRECOMP_HPP__
+#define __OPENCV_PERF_PRECOMP_HPP__
+
+#include "opencv2/ts.hpp"
+#include "opencv2/photo.hpp"
+#include "opencv2/highgui.hpp"
+
+#ifdef GTEST_CREATE_SHARED_LIBRARY
+#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
+#endif
+
+#endif

modules/optim/src/arrays.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective icvers.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_DENOISING_ARRAYS_HPP__
+#define __OPENCV_DENOISING_ARRAYS_HPP__
+
+template <class T> struct Array2d {
+    T* a;
+    int n1,n2;
+    bool needToDeallocArray;
+
+    Array2d(const Array2d& array2d):
+        a(array2d.a), n1(array2d.n1), n2(array2d.n2), needToDeallocArray(false)
+    {
+        if (array2d.needToDeallocArray) {
+            // copy constructor for self allocating arrays not supported
+            throw new std::exception();
+        }
+    }
+
+    Array2d(T* _a, int _n1, int _n2):
+        a(_a), n1(_n1), n2(_n2), needToDeallocArray(false) {}
+
+    Array2d(int _n1, int _n2):
+        n1(_n1), n2(_n2), needToDeallocArray(true)
+    {
+        a = new T[n1*n2];
+    }
+
+    ~Array2d() {
+        if (needToDeallocArray) {
+            delete[] a;
+        }
+    }
+
+    T* operator [] (int i) {
+        return a + i*n2;
+    }
+
+    inline T* row_ptr(int i) {
+        return (*this)[i];
+    }
+};
+
+template <class T> struct Array3d {
+    T* a;
+    int n1,n2,n3;
+    bool needToDeallocArray;
+
+    Array3d(T* _a, int _n1, int _n2, int _n3):
+        a(_a), n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(false) {}
+
+    Array3d(int _n1, int _n2, int _n3):
+        n1(_n1), n2(_n2), n3(_n3), needToDeallocArray(true)
+    {
+        a = new T[n1*n2*n3];
+    }
+
+    ~Array3d() {
+        if (needToDeallocArray) {
+            delete[] a;
+        }
+    }
+
+    Array2d<T> operator [] (int i) {
+        Array2d<T> array2d(a + i*n2*n3, n2, n3);
+        return array2d;
+    }
+
+    inline T* row_ptr(int i1, int i2) {
+        return a + i1*n2*n3 + i2*n3;
+    }
+};
+
+template <class T> struct Array4d {
+    T* a;
+    int n1,n2,n3,n4;
+    bool needToDeallocArray;
+    int steps[4];
+
+    void init_steps() {
+        steps[0] = n2*n3*n4;
+        steps[1] = n3*n4;
+        steps[2] = n4;
+        steps[3] = 1;
+    }
+
+    Array4d(T* _a, int _n1, int _n2, int _n3, int _n4):
+        a(_a), n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(false)
+     {
+        init_steps();
+     }
+
+    Array4d(int _n1, int _n2, int _n3, int _n4):
+        n1(_n1), n2(_n2), n3(_n3), n4(_n4), needToDeallocArray(true)
+    {
+        a = new T[n1*n2*n3*n4];
+        init_steps();
+   }
+
+    ~Array4d() {
+        if (needToDeallocArray) {
+            delete[] a;
+        }
+    }
+
+    Array3d<T> operator [] (int i) {
+        Array3d<T> array3d(a + i*n2*n3*n4, n2, n3, n4);
+        return array3d;
+    }
+
+    inline T* row_ptr(int i1, int i2, int i3) {
+        return a + i1*n2*n3*n4 + i2*n3*n4 + i3*n4;
+    }
+
+    inline int step_size(int dimension) {
+        return steps[dimension];
+    }
+};
+
+#endif
+
+

modules/optim/src/denoising.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective icvers.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+#include "opencv2/photo.hpp"
+#include "opencv2/imgproc.hpp"
+#include "fast_nlmeans_denoising_invoker.hpp"
+#include "fast_nlmeans_multi_denoising_invoker.hpp"
+
+void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
+                               int templateWindowSize, int searchWindowSize)
+{
+    Mat src = _src.getMat();
+    _dst.create(src.size(), src.type());
+    Mat dst = _dst.getMat();
+
+#ifdef HAVE_TEGRA_OPTIMIZATION
+    if(tegra::fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize))
+        return;
+#endif
+
+    switch (src.type()) {
+        case CV_8U:
+            parallel_for(cv::BlockedRange(0, src.rows),
+                FastNlMeansDenoisingInvoker<uchar>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for(cv::BlockedRange(0, src.rows),
+                FastNlMeansDenoisingInvoker<cv::Vec2b>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for(cv::BlockedRange(0, src.rows),
+                FastNlMeansDenoisingInvoker<cv::Vec3b>(
+                    src, dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supported");
+    }
+}
+
+void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
+                                      float h, float hForColorComponents,
+                                      int templateWindowSize, int searchWindowSize)
+{
+    Mat src = _src.getMat();
+    _dst.create(src.size(), src.type());
+    Mat dst = _dst.getMat();
+
+    if (src.type() != CV_8UC3) {
+        CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!");
+        return;
+    }
+
+    Mat src_lab;
+    cvtColor(src, src_lab, COLOR_LBGR2Lab);
+
+    Mat l(src.size(), CV_8U);
+    Mat ab(src.size(), CV_8UC2);
+    Mat l_ab[] = { l, ab };
+    int from_to[] = { 0,0, 1,1, 2,2 };
+    mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
+
+    fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
+    fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
+
+    Mat l_ab_denoised[] = { l, ab };
+    Mat dst_lab(src.size(), src.type());
+    mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
+
+    cvtColor(dst_lab, dst, COLOR_Lab2LBGR);
+}
+
+static void fastNlMeansDenoisingMultiCheckPreconditions(
+                               const std::vector<Mat>& srcImgs,
+                               int imgToDenoiseIndex, int temporalWindowSize,
+                               int templateWindowSize, int searchWindowSize)
+{
+    int src_imgs_size = (int)srcImgs.size();
+    if (src_imgs_size == 0) {
+        CV_Error(Error::StsBadArg, "Input images vector should not be empty!");
+    }
+
+    if (temporalWindowSize % 2 == 0 ||
+        searchWindowSize % 2 == 0 ||
+        templateWindowSize % 2 == 0) {
+        CV_Error(Error::StsBadArg, "All windows sizes should be odd!");
+    }
+
+    int temporalWindowHalfSize = temporalWindowSize / 2;
+    if (imgToDenoiseIndex - temporalWindowHalfSize < 0 ||
+        imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size)
+    {
+        CV_Error(Error::StsBadArg,
+            "imgToDenoiseIndex and temporalWindowSize "
+            "should be choosen corresponding srcImgs size!");
+    }
+
+    for (int i = 1; i < src_imgs_size; i++) {
+        if (srcImgs[0].size() != srcImgs[i].size() || srcImgs[0].type() != srcImgs[i].type()) {
+            CV_Error(Error::StsBadArg, "Input images should have the same size and type!");
+        }
+    }
+}
+
+void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
+                                    int imgToDenoiseIndex, int temporalWindowSize,
+                                    float h, int templateWindowSize, int searchWindowSize)
+{
+    std::vector<Mat> srcImgs;
+    _srcImgs.getMatVector(srcImgs);
+
+    fastNlMeansDenoisingMultiCheckPreconditions(
+        srcImgs, imgToDenoiseIndex,
+        temporalWindowSize, templateWindowSize, searchWindowSize
+    );
+    _dst.create(srcImgs[0].size(), srcImgs[0].type());
+    Mat dst = _dst.getMat();
+
+    switch (srcImgs[0].type()) {
+        case CV_8U:
+            parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<uchar>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC2:
+            parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec2b>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        case CV_8UC3:
+            parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
+                FastNlMeansMultiDenoisingInvoker<cv::Vec3b>(
+                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
+                    dst, templateWindowSize, searchWindowSize, h));
+            break;
+        default:
+            CV_Error(Error::StsBadArg,
+                "Unsupported matrix format! Only uchar, Vec2b, Vec3b are supported");
+    }
+}
+
+void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
+                                           int imgToDenoiseIndex, int temporalWindowSize,
+                                           float h, float hForColorComponents,
+                                           int templateWindowSize, int searchWindowSize)
+{
+    std::vector<Mat> srcImgs;
+    _srcImgs.getMatVector(srcImgs);
+
+    fastNlMeansDenoisingMultiCheckPreconditions(
+        srcImgs, imgToDenoiseIndex,
+        temporalWindowSize, templateWindowSize, searchWindowSize
+    );
+
+    _dst.create(srcImgs[0].size(), srcImgs[0].type());
+    Mat dst = _dst.getMat();
+
+    int src_imgs_size = (int)srcImgs.size();
+
+    if (srcImgs[0].type() != CV_8UC3) {
+        CV_Error(Error::StsBadArg, "Type of input images should be CV_8UC3!");
+        return;
+    }
+
+    int from_to[] = { 0,0, 1,1, 2,2 };
+
+    // TODO convert only required images
+    std::vector<Mat> src_lab(src_imgs_size);
+    std::vector<Mat> l(src_imgs_size);
+    std::vector<Mat> ab(src_imgs_size);
+    for (int i = 0; i < src_imgs_size; i++) {
+        src_lab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC3);
+        l[i] = Mat::zeros(srcImgs[0].size(), CV_8UC1);
+        ab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC2);
+        cvtColor(srcImgs[i], src_lab[i], COLOR_LBGR2Lab);
+
+        Mat l_ab[] = { l[i], ab[i] };
+        mixChannels(&src_lab[i], 1, l_ab, 2, from_to, 3);
+    }
+
+    Mat dst_l;
+    Mat dst_ab;
+
+    fastNlMeansDenoisingMulti(
+        l, dst_l, imgToDenoiseIndex, temporalWindowSize,
+        h, templateWindowSize, searchWindowSize);
+
+    fastNlMeansDenoisingMulti(
+        ab, dst_ab, imgToDenoiseIndex, temporalWindowSize,
+        hForColorComponents, templateWindowSize, searchWindowSize);
+
+    Mat l_ab_denoised[] = { dst_l, dst_ab };
+    Mat dst_lab(srcImgs[0].size(), srcImgs[0].type());
+    mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
+
+    cvtColor(dst_lab, dst, COLOR_Lab2LBGR);
+}
+
+

modules/optim/src/fast_nlmeans_denoising_invoker.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective icvers.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_HPP__
+#define __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_HPP__
+
+#include "precomp.hpp"
+#include <limits>
+
+#include "fast_nlmeans_denoising_invoker_commons.hpp"
+#include "arrays.hpp"
+
+using namespace cv;
+
+template <typename T>
+struct FastNlMeansDenoisingInvoker {
+    public:
+        FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
+            int template_window_size, int search_window_size, const float h);
+
+        void operator() (const BlockedRange& range) const;
+
+    private:
+        void operator= (const FastNlMeansDenoisingInvoker&);
+
+        const Mat& src_;
+        Mat& dst_;
+
+        Mat extended_src_;
+        int border_size_;
+
+        int template_window_size_;
+        int search_window_size_;
+
+        int template_window_half_size_;
+        int search_window_half_size_;
+
+        int fixed_point_mult_;
+        int almost_template_window_size_sq_bin_shift_;
+        std::vector<int> almost_dist2weight_;
+
+        void calcDistSumsForFirstElementInRow(
+            int i,
+            Array2d<int>& dist_sums,
+            Array3d<int>& col_dist_sums,
+            Array3d<int>& up_col_dist_sums) const;
+
+        void calcDistSumsForElementInFirstRow(
+            int i,
+            int j,
+            int first_col_num,
+            Array2d<int>& dist_sums,
+            Array3d<int>& col_dist_sums,
+            Array3d<int>& up_col_dist_sums) const;
+};
+
+inline int getNearestPowerOf2(int value)
+{
+    int p = 0;
+    while( 1 << p < value) ++p;
+    return p;
+}
+
+template <class T>
+FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
+    const cv::Mat& src,
+    cv::Mat& dst,
+    int template_window_size,
+    int search_window_size,
+    const float h) : src_(src), dst_(dst)
+{
+    CV_Assert(src.channels() == sizeof(T)); //T is Vec1b or Vec2b or Vec3b
+
+    template_window_half_size_ = template_window_size / 2;
+    search_window_half_size_   = search_window_size   / 2;
+    template_window_size_      = template_window_half_size_ * 2 + 1;
+    search_window_size_        = search_window_half_size_   * 2 + 1;
+
+    border_size_ = search_window_half_size_ + template_window_half_size_;
+    copyMakeBorder(src_, extended_src_,
+        border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
+
+    const int max_estimate_sum_value = search_window_size_ * search_window_size_ * 255;
+    fixed_point_mult_ = std::numeric_limits<int>::max() / max_estimate_sum_value;
+
+    // precalc weight for every possible l2 dist between blocks
+    // additional optimization of precalced weights to replace division(averaging) by binary shift
+
+    CV_Assert(template_window_size_ <= 46340 ); // sqrt(INT_MAX)
+    int template_window_size_sq = template_window_size_ * template_window_size_;
+    almost_template_window_size_sq_bin_shift_ = getNearestPowerOf2(template_window_size_sq);
+    double almost_dist2actual_dist_multiplier = ((double)(1 << almost_template_window_size_sq_bin_shift_)) / template_window_size_sq;
+
+    int max_dist = 255 * 255 * sizeof(T);
+    int almost_max_dist = (int) (max_dist / almost_dist2actual_dist_multiplier + 1);
+    almost_dist2weight_.resize(almost_max_dist);
+
+    const double WEIGHT_THRESHOLD = 0.001;
+    for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
+        double dist = almost_dist * almost_dist2actual_dist_multiplier;
+        int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
+
+        if (weight < WEIGHT_THRESHOLD * fixed_point_mult_)
+            weight = 0;
+
+        almost_dist2weight_[almost_dist] = weight;
+    }
+    CV_Assert(almost_dist2weight_[0] == fixed_point_mult_);
+    // additional optimization init end
+
+    if (dst_.empty()) {
+        dst_ = Mat::zeros(src_.size(), src_.type());
+    }
+}
+
+template <class T>
+void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
+    int row_from = range.begin();
+    int row_to = range.end() - 1;
+
+    Array2d<int> dist_sums(search_window_size_, search_window_size_);
+
+    // for lazy calc optimization
+    Array3d<int> col_dist_sums(template_window_size_, search_window_size_, search_window_size_);
+
+    int first_col_num = -1;
+    Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_);
+
+    for (int i = row_from; i <= row_to; i++) {
+        for (int j = 0; j < src_.cols; j++) {
+            int search_window_y = i - search_window_half_size_;
+            int search_window_x = j - search_window_half_size_;
+
+            // calc dist_sums
+            if (j == 0) {
+                calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
+                first_col_num = 0;
+
+            } else { // calc cur dist_sums using previous dist_sums
+                if (i == row_from) {
+                    calcDistSumsForElementInFirstRow(i, j, first_col_num,
+                        dist_sums, col_dist_sums, up_col_dist_sums);
+
+                } else {
+                    int ay = border_size_ + i;
+                    int ax = border_size_ + j + template_window_half_size_;
+
+                    int start_by =
+                        border_size_ + i - search_window_half_size_;
+
+                    int start_bx =
+                        border_size_ + j - search_window_half_size_ + template_window_half_size_;
+
+                    T a_up = extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
+                    T a_down = extended_src_.at<T>(ay + template_window_half_size_, ax);
+
+                    // copy class member to local variable for optimization
+                    int search_window_size = search_window_size_;
+
+                    for (int y = 0; y < search_window_size; y++) {
+                        int* dist_sums_row = dist_sums.row_ptr(y);
+
+                        int* col_dist_sums_row = col_dist_sums.row_ptr(first_col_num,y);
+
+                        int* up_col_dist_sums_row = up_col_dist_sums.row_ptr(j, y);
+
+                        const T* b_up_ptr =
+                            extended_src_.ptr<T>(start_by - template_window_half_size_ - 1 + y);
+
+                        const T* b_down_ptr =
+                            extended_src_.ptr<T>(start_by + template_window_half_size_ + y);
+
+                        for (int x = 0; x < search_window_size; x++) {
+                            dist_sums_row[x] -= col_dist_sums_row[x];
+
+                            col_dist_sums_row[x] =
+                                up_col_dist_sums_row[x] +
+                                calcUpDownDist(
+                                    a_up, a_down,
+                                    b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]
+                                );
+
+                            dist_sums_row[x] += col_dist_sums_row[x];
+
+                            up_col_dist_sums_row[x] = col_dist_sums_row[x];
+
+                        }
+                    }
+                }
+
+                first_col_num = (first_col_num + 1) % template_window_size_;
+            }
+
+            // calc weights
+            int weights_sum = 0;
+
+            int estimation[3];
+            for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
+                estimation[channel_num] = 0;
+            }
+
+            for (int y = 0; y < search_window_size_; y++) {
+                const T* cur_row_ptr = extended_src_.ptr<T>(border_size_ + search_window_y + y);
+                int* dist_sums_row = dist_sums.row_ptr(y);
+                for (int x = 0; x < search_window_size_; x++) {
+                    int almostAvgDist =
+                        dist_sums_row[x] >> almost_template_window_size_sq_bin_shift_;
+
+                    int weight = almost_dist2weight_[almostAvgDist];
+                    weights_sum += weight;
+
+                    T p = cur_row_ptr[border_size_ + search_window_x + x];
+                    incWithWeight(estimation, weight, p);
+                }
+            }
+
+            for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
+                estimation[channel_num] = ((unsigned)estimation[channel_num] + weights_sum/2) / weights_sum;
+
+            dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
+        }
+    }
+}
+
+template <class T>
+inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
+    int i,
+    Array2d<int>& dist_sums,
+    Array3d<int>& col_dist_sums,
+    Array3d<int>& up_col_dist_sums) const
+{
+    int j = 0;
+
+    for (int y = 0; y < search_window_size_; y++) {
+        for (int x = 0; x < search_window_size_; x++) {
+            dist_sums[y][x] = 0;
+            for (int tx = 0; tx < template_window_size_; tx++) {
+                col_dist_sums[tx][y][x] = 0;
+            }
+
+            int start_y = i + y - search_window_half_size_;
+            int start_x = j + x - search_window_half_size_;
+
+            for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
+                for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
+                    int dist = calcDist<T>(extended_src_,
+                        border_size_ + i + ty, border_size_ + j + tx,
+                        border_size_ + start_y + ty, border_size_ + start_x + tx);
+
+                    dist_sums[y][x] += dist;
+                    col_dist_sums[tx + template_window_half_size_][y][x] += dist;
+                }
+            }
+
+            up_col_dist_sums[j][y][x] = col_dist_sums[template_window_size_ - 1][y][x];
+        }
+    }
+}
+
+template <class T>
+inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
+    int i,
+    int j,
+    int first_col_num,
+    Array2d<int>& dist_sums,
+    Array3d<int>& col_dist_sums,
+    Array3d<int>& up_col_dist_sums) const
+{
+    int ay = border_size_ + i;
+    int ax = border_size_ + j + template_window_half_size_;
+
+    int start_by = border_size_ + i - search_window_half_size_;
+    int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
+
+    int new_last_col_num = first_col_num;
+
+    for (int y = 0; y < search_window_size_; y++) {
+        for (int x = 0; x < search_window_size_; x++) {
+            dist_sums[y][x] -= col_dist_sums[first_col_num][y][x];
+
+            col_dist_sums[new_last_col_num][y][x] = 0;
+            int by = start_by + y;
+            int bx = start_bx + x;
+            for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
+                col_dist_sums[new_last_col_num][y][x] +=
+                    calcDist<T>(extended_src_, ay + ty, ax, by + ty, bx);
+            }
+
+            dist_sums[y][x] += col_dist_sums[new_last_col_num][y][x];
+
+            up_col_dist_sums[j][y][x] = col_dist_sums[new_last_col_num][y][x];
+        }
+    }
+}
+
+#endif

modules/optim/src/fast_nlmeans_denoising_invoker_commons.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective icvers.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_COMMONS_HPP__
+#define __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_COMMONS_HPP__
+
+using namespace cv;
+
+template <typename T> static inline int calcDist(const T a, const T b);
+
+template <> inline int calcDist(const uchar a, const uchar b) {
+    return (a-b) * (a-b);
+}
+
+template <> inline int calcDist(const Vec2b a, const Vec2b b) {
+    return (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1]);
+}
+
+template <> inline int calcDist(const Vec3b a, const Vec3b b) {
+    return (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1]) + (a[2]-b[2])*(a[2]-b[2]);
+}
+
+template <typename T> static inline int calcDist(const Mat& m, int i1, int j1, int i2, int j2) {
+    const T a = m.at<T>(i1, j1);
+    const T b = m.at<T>(i2, j2);
+    return calcDist<T>(a,b);
+}
+
+template <typename T> static inline int calcUpDownDist(T a_up, T a_down, T b_up, T b_down) {
+    return calcDist(a_down,b_down) - calcDist(a_up, b_up);
+}
+
+template <> inline int calcUpDownDist(uchar a_up, uchar a_down, uchar  b_up, uchar b_down) {
+    int A = a_down - b_down;
+    int B = a_up - b_up;
+    return (A-B)*(A+B);
+}
+
+template <typename T> static inline void incWithWeight(int* estimation, int weight, T p);
+
+template <> inline void incWithWeight(int* estimation, int weight, uchar p) {
+    estimation[0] += weight * p;
+}
+
+template <> inline void incWithWeight(int* estimation, int weight, Vec2b p) {
+    estimation[0] += weight * p[0];
+    estimation[1] += weight * p[1];
+}
+
+template <> inline void incWithWeight(int* estimation, int weight, Vec3b p) {
+    estimation[0] += weight * p[0];
+    estimation[1] += weight * p[1];
+    estimation[2] += weight * p[2];
+}
+
+template <typename T> static inline T saturateCastFromArray(int* estimation);
+
+template <> inline uchar saturateCastFromArray(int* estimation) {
+    return saturate_cast<uchar>(estimation[0]);
+}
+
+template <> inline Vec2b saturateCastFromArray(int* estimation) {
+    Vec2b res;
+    res[0] = saturate_cast<uchar>(estimation[0]);
+    res[1] = saturate_cast<uchar>(estimation[1]);
+    return res;
+}
+
+template <> inline Vec3b saturateCastFromArray(int* estimation) {
+    Vec3b res;
+    res[0] = saturate_cast<uchar>(estimation[0]);
+    res[1] = saturate_cast<uchar>(estimation[1]);
+    res[2] = saturate_cast<uchar>(estimation[2]);
+    return res;
+}
+
+#endif

modules/optim/src/fast_nlmeans_multi_denoising_invoker.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective icvers.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
+#define __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
+
+#include "precomp.hpp"
+#include <limits>
+
+#include "fast_nlmeans_denoising_invoker_commons.hpp"
+#include "arrays.hpp"
+
+using namespace cv;
+
+template <typename T>
+struct FastNlMeansMultiDenoisingInvoker {
+    public:
+        FastNlMeansMultiDenoisingInvoker(
+            const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize,
+            Mat& dst, int template_window_size, int search_window_size, const float h);
+
+        void operator() (const BlockedRange& range) const;
+
+    private:
+        void operator= (const FastNlMeansMultiDenoisingInvoker&);
+
+        int rows_;
+        int cols_;
+
+        Mat& dst_;
+
+        std::vector<Mat> extended_srcs_;
+        Mat main_extended_src_;
+        int border_size_;
+
+        int template_window_size_;
+        int search_window_size_;
+        int temporal_window_size_;
+
+        int template_window_half_size_;
+        int search_window_half_size_;
+        int temporal_window_half_size_;
+
+        int fixed_point_mult_;
+        int almost_template_window_size_sq_bin_shift;
+        std::vector<int> almost_dist2weight;
+
+        void calcDistSumsForFirstElementInRow(
+            int i,
+            Array3d<int>& dist_sums,
+            Array4d<int>& col_dist_sums,
+            Array4d<int>& up_col_dist_sums) const;
+
+        void calcDistSumsForElementInFirstRow(
+            int i,
+            int j,
+            int first_col_num,
+            Array3d<int>& dist_sums,
+            Array4d<int>& col_dist_sums,
+            Array4d<int>& up_col_dist_sums) const;
+};
+
+template <class T>
+FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
+    const std::vector<Mat>& srcImgs,
+    int imgToDenoiseIndex,
+    int temporalWindowSize,
+    cv::Mat& dst,
+    int template_window_size,
+    int search_window_size,
+    const float h) : dst_(dst), extended_srcs_(srcImgs.size())
+{
+    CV_Assert(srcImgs.size() > 0);
+    CV_Assert(srcImgs[0].channels() == sizeof(T));
+
+    rows_ = srcImgs[0].rows;
+    cols_ = srcImgs[0].cols;
+
+    template_window_half_size_ = template_window_size / 2;
+    search_window_half_size_ = search_window_size / 2;
+    temporal_window_half_size_ = temporalWindowSize / 2;
+
+    template_window_size_ = template_window_half_size_ * 2 + 1;
+    search_window_size_ = search_window_half_size_ * 2 + 1;
+    temporal_window_size_ = temporal_window_half_size_ * 2 + 1;
+
+    border_size_ = search_window_half_size_ + template_window_half_size_;
+    for (int i = 0; i < temporal_window_size_; i++) {
+        copyMakeBorder(
+            srcImgs[imgToDenoiseIndex - temporal_window_half_size_ + i], extended_srcs_[i],
+            border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
+    }
+    main_extended_src_ = extended_srcs_[temporal_window_half_size_];
+
+    const int max_estimate_sum_value =
+        temporal_window_size_ * search_window_size_ * search_window_size_ * 255;
+
+    fixed_point_mult_ = std::numeric_limits<int>::max() / max_estimate_sum_value;
+
+    // precalc weight for every possible l2 dist between blocks
+    // additional optimization of precalced weights to replace division(averaging) by binary shift
+    int template_window_size_sq = template_window_size_ * template_window_size_;
+    almost_template_window_size_sq_bin_shift = 0;
+    while (1 << almost_template_window_size_sq_bin_shift < template_window_size_sq) {
+        almost_template_window_size_sq_bin_shift++;
+    }
+
+    int almost_template_window_size_sq = 1 << almost_template_window_size_sq_bin_shift;
+    double almost_dist2actual_dist_multiplier =
+        ((double) almost_template_window_size_sq) / template_window_size_sq;
+
+    int max_dist = 255 * 255 * sizeof(T);
+    int almost_max_dist = (int) (max_dist / almost_dist2actual_dist_multiplier + 1);
+    almost_dist2weight.resize(almost_max_dist);
+
+    const double WEIGHT_THRESHOLD = 0.001;
+    for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
+        double dist = almost_dist * almost_dist2actual_dist_multiplier;
+        int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
+
+        if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
+            weight = 0;
+        }
+
+        almost_dist2weight[almost_dist] = weight;
+    }
+    CV_Assert(almost_dist2weight[0] == fixed_point_mult_);
+    // additional optimization init end
+
+    if (dst_.empty()) {
+        dst_ = Mat::zeros(srcImgs[0].size(), srcImgs[0].type());
+    }
+}
+
+template <class T>
+void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
+    int row_from = range.begin();
+    int row_to = range.end() - 1;
+
+    Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
+
+    // for lazy calc optimization
+    Array4d<int> col_dist_sums(
+        template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
+
+    int first_col_num = -1;
+
+    Array4d<int> up_col_dist_sums(
+        cols_, temporal_window_size_, search_window_size_, search_window_size_);
+
+    for (int i = row_from; i <= row_to; i++) {
+        for (int j = 0; j < cols_; j++) {
+            int search_window_y = i - search_window_half_size_;
+            int search_window_x = j - search_window_half_size_;
+
+            // calc dist_sums
+            if (j == 0) {
+                calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
+                first_col_num = 0;
+
+            } else { // calc cur dist_sums using previous dist_sums
+                if (i == row_from) {
+                    calcDistSumsForElementInFirstRow(i, j, first_col_num,
+                        dist_sums, col_dist_sums, up_col_dist_sums);
+
+                } else {
+                    int ay = border_size_ + i;
+                    int ax = border_size_ + j + template_window_half_size_;
+
+                    int start_by =
+                        border_size_ + i - search_window_half_size_;
+
+                    int start_bx =
+                        border_size_ + j - search_window_half_size_ + template_window_half_size_;
+
+                    T a_up = main_extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
+                    T a_down = main_extended_src_.at<T>(ay + template_window_half_size_, ax);
+
+                    // copy class member to local variable for optimization
+                    int search_window_size = search_window_size_;
+
+                    for (int d = 0; d < temporal_window_size_; d++) {
+                        Mat cur_extended_src = extended_srcs_[d];
+                        Array2d<int> cur_dist_sums = dist_sums[d];
+                        Array2d<int> cur_col_dist_sums = col_dist_sums[first_col_num][d];
+                        Array2d<int> cur_up_col_dist_sums = up_col_dist_sums[j][d];
+                        for (int y = 0; y < search_window_size; y++) {
+                            int* dist_sums_row = cur_dist_sums.row_ptr(y);
+
+                            int* col_dist_sums_row = cur_col_dist_sums.row_ptr(y);
+
+                            int* up_col_dist_sums_row = cur_up_col_dist_sums.row_ptr(y);
+
+                            const T* b_up_ptr =
+                                cur_extended_src.ptr<T>(start_by - template_window_half_size_ - 1 + y);
+                            const T* b_down_ptr =
+                                cur_extended_src.ptr<T>(start_by + template_window_half_size_ + y);
+
+                            for (int x = 0; x < search_window_size; x++) {
+                                dist_sums_row[x] -= col_dist_sums_row[x];
+
+                                col_dist_sums_row[x] = up_col_dist_sums_row[x] +
+                                    calcUpDownDist(
+                                        a_up, a_down,
+                                        b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]
+                                    );
+
+                                dist_sums_row[x] += col_dist_sums_row[x];
+
+                                up_col_dist_sums_row[x] = col_dist_sums_row[x];
+
+                            }
+                        }
+                    }
+                }
+
+                first_col_num = (first_col_num + 1) % template_window_size_;
+            }
+
+            // calc weights
+            int weights_sum = 0;
+
+            int estimation[3];
+            for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
+                estimation[channel_num] = 0;
+            }
+            for (int d = 0; d < temporal_window_size_; d++) {
+                const Mat& esrc_d = extended_srcs_[d];
+                for (int y = 0; y < search_window_size_; y++) {
+                    const T* cur_row_ptr = esrc_d.ptr<T>(border_size_ + search_window_y + y);
+
+                    int* dist_sums_row = dist_sums.row_ptr(d, y);
+
+                    for (int x = 0; x < search_window_size_; x++) {
+                        int almostAvgDist =
+                            dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
+
+                        int weight = almost_dist2weight[almostAvgDist];
+                        weights_sum += weight;
+
+                        T p = cur_row_ptr[border_size_ + search_window_x + x];
+                        incWithWeight(estimation, weight, p);
+                    }
+                }
+            }
+
+            for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
+                estimation[channel_num] = ((unsigned)estimation[channel_num] + weights_sum / 2) / weights_sum;
+
+            dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
+
+        }
+    }
+}
+
+template <class T>
+inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
+    int i,
+    Array3d<int>& dist_sums,
+    Array4d<int>& col_dist_sums,
+    Array4d<int>& up_col_dist_sums) const
+{
+    int j = 0;
+
+    for (int d = 0; d < temporal_window_size_; d++) {
+        Mat cur_extended_src = extended_srcs_[d];
+        for (int y = 0; y < search_window_size_; y++) {
+            for (int x = 0; x < search_window_size_; x++) {
+                dist_sums[d][y][x] = 0;
+                for (int tx = 0; tx < template_window_size_; tx++) {
+                    col_dist_sums[tx][d][y][x] = 0;
+                }
+
+                int start_y = i + y - search_window_half_size_;
+                int start_x = j + x - search_window_half_size_;
+
+                int* dist_sums_ptr = &dist_sums[d][y][x];
+                int* col_dist_sums_ptr = &col_dist_sums[0][d][y][x];
+                int col_dist_sums_step = col_dist_sums.step_size(0);
+                for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
+                    for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
+                        int dist = calcDist<T>(
+                            main_extended_src_.at<T>(
+                                border_size_ + i + ty, border_size_ + j + tx),
+                            cur_extended_src.at<T>(
+                                border_size_ + start_y + ty, border_size_ + start_x + tx)
+                        );
+
+                        *dist_sums_ptr += dist;
+                        *col_dist_sums_ptr += dist;
+                    }
+                    col_dist_sums_ptr += col_dist_sums_step;
+                }
+
+                up_col_dist_sums[j][d][y][x] = col_dist_sums[template_window_size_ - 1][d][y][x];
+            }
+        }
+    }
+}
+
+template <class T>
+inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
+    int i,
+    int j,
+    int first_col_num,
+    Array3d<int>& dist_sums,
+    Array4d<int>& col_dist_sums,
+    Array4d<int>& up_col_dist_sums) const
+{
+    int ay = border_size_ + i;
+    int ax = border_size_ + j + template_window_half_size_;
+
+    int start_by = border_size_ + i - search_window_half_size_;
+    int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
+
+    int new_last_col_num = first_col_num;
+
+    for (int d = 0; d < temporal_window_size_; d++) {
+        Mat cur_extended_src = extended_srcs_[d];
+        for (int y = 0; y < search_window_size_; y++) {
+            for (int x = 0; x < search_window_size_; x++) {
+                dist_sums[d][y][x] -= col_dist_sums[first_col_num][d][y][x];
+
+                col_dist_sums[new_last_col_num][d][y][x] = 0;
+                int by = start_by + y;
+                int bx = start_bx + x;
+
+                int* col_dist_sums_ptr = &col_dist_sums[new_last_col_num][d][y][x];
+                for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
+                    *col_dist_sums_ptr +=
+                        calcDist<T>(
+                            main_extended_src_.at<T>(ay + ty, ax),
+                            cur_extended_src.at<T>(by + ty, bx)
+                        );
+                }
+
+                dist_sums[d][y][x] += col_dist_sums[new_last_col_num][d][y][x];
+
+                up_col_dist_sums[j][d][y][x] = col_dist_sums[new_last_col_num][d][y][x];
+            }
+        }
+    }
+}
+
+#endif

modules/optim/src/precomp.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+/* End of file. */

modules/optim/src/precomp.hpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_PRECOMP_H__
+#define __OPENCV_PRECOMP_H__
+
+#include "opencv2/photo.hpp"
+#include "opencv2/core/private.hpp"
+
+#ifdef HAVE_TEGRA_OPTIMIZATION
+#include "opencv2/photo/photo_tegra.hpp"
+#endif
+
+#endif

modules/optim/test/test_denoising.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include "opencv2/photo.hpp"
+#include <string>
+
+using namespace cv;
+using namespace std;
+
+//#define DUMP_RESULTS
+
+#ifdef DUMP_RESULTS
+#  define DUMP(image, path) imwrite(path, image)
+#else
+#  define DUMP(image, path)
+#endif
+
+
+TEST(Photo_DenoisingGrayscale, regression)
+{
+    string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
+    string original_path = folder + "lena_noised_gaussian_sigma=10.png";
+    string expected_path = folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png";
+
+    Mat original = imread(original_path, IMREAD_GRAYSCALE);
+    Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
+
+    ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
+    ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
+
+    Mat result;
+    fastNlMeansDenoising(original, result, 10);
+
+    DUMP(result, expected_path + ".res.png");
+
+    ASSERT_EQ(0, norm(result != expected));
+}
+
+TEST(Photo_DenoisingColored, regression)
+{
+    string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
+    string original_path = folder + "lena_noised_gaussian_sigma=10.png";
+    string expected_path = folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png";
+
+    Mat original = imread(original_path, IMREAD_COLOR);
+    Mat expected = imread(expected_path, IMREAD_COLOR);
+
+    ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
+    ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
+
+    Mat result;
+    fastNlMeansDenoisingColored(original, result, 10, 10);
+
+    DUMP(result, expected_path + ".res.png");
+
+    ASSERT_EQ(0, norm(result != expected));
+}
+
+TEST(Photo_DenoisingGrayscaleMulti, regression)
+{
+    const int imgs_count = 3;
+    string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
+
+    string expected_path = folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png";
+    Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
+
+    vector<Mat> original(imgs_count);
+    for (int i = 0; i < imgs_count; i++)
+    {
+        string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
+        original[i] = imread(original_path, IMREAD_GRAYSCALE);
+        ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
+    }
+
+    Mat result;
+    fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15);
+
+    DUMP(result, expected_path + ".res.png");
+
+    ASSERT_EQ(0, norm(result != expected));
+}
+
+TEST(Photo_DenoisingColoredMulti, regression)
+{
+    const int imgs_count = 3;
+    string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
+
+    string expected_path = folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png";
+    Mat expected = imread(expected_path, IMREAD_COLOR);
+    ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
+
+    vector<Mat> original(imgs_count);
+    for (int i = 0; i < imgs_count; i++)
+    {
+        string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
+        original[i] = imread(original_path, IMREAD_COLOR);
+        ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
+    }
+
+    Mat result;
+    fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15);
+
+    DUMP(result, expected_path + ".res.png");
+
+    ASSERT_EQ(0, norm(result != expected));
+}
+
+TEST(Photo_White, issue_2646)
+{
+    cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255));
+    cv::Mat filtered;
+    cv::fastNlMeansDenoising(img, filtered);
+
+    int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img);
+
+    ASSERT_EQ(0, nonWhitePixelsCount);
+}

modules/optim/test/test_inpaint.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include <string>
+
+using namespace std;
+using namespace cv;
+
+class CV_InpaintTest : public cvtest::BaseTest
+{
+public:
+    CV_InpaintTest();
+    ~CV_InpaintTest();
+protected:
+    void run(int);
+};
+
+CV_InpaintTest::CV_InpaintTest()
+{
+}
+CV_InpaintTest::~CV_InpaintTest() {}
+
+void CV_InpaintTest::run( int )
+{
+    string folder = string(ts->get_data_path()) + "inpaint/";
+    Mat orig = imread(folder + "orig.png");
+    Mat exp1 = imread(folder + "exp1.png");
+    Mat exp2 = imread(folder + "exp2.png");
+    Mat mask = imread(folder + "mask.png");
+
+    if (orig.empty() || exp1.empty() || exp2.empty() || mask.empty())
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
+        return;
+    }
+
+    Mat inv_mask;
+    mask.convertTo(inv_mask, CV_8UC3, -1.0, 255.0);
+
+    Mat mask1ch;
+    cv::cvtColor(mask, mask1ch, COLOR_BGR2GRAY);
+
+    Mat test = orig.clone();
+    test.setTo(Scalar::all(255), mask1ch);
+
+    Mat res1, res2;
+    inpaint( test, mask1ch, res1, 5, INPAINT_NS );
+    inpaint( test, mask1ch, res2, 5, INPAINT_TELEA );
+
+    Mat diff1, diff2;
+    absdiff( orig, res1, diff1 );
+    absdiff( orig, res2, diff2 );
+
+    double n1 = norm(diff1.reshape(1), NORM_INF, inv_mask.reshape(1));
+    double n2 = norm(diff2.reshape(1), NORM_INF, inv_mask.reshape(1));
+
+    if (n1 != 0 || n2 != 0)
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
+        return;
+    }
+
+    absdiff( exp1, res1, diff1 );
+    absdiff( exp2, res2, diff2 );
+
+    n1 = norm(diff1.reshape(1), NORM_INF, mask.reshape(1));
+    n2 = norm(diff2.reshape(1), NORM_INF, mask.reshape(1));
+
+    const int jpeg_thres = 3;
+    if (n1 > jpeg_thres || n2 > jpeg_thres)
+    {
+        ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
+        return;
+    }
+
+    ts->set_failed_test_info(cvtest::TS::OK);
+}
+
+TEST(Photo_Inpaint, regression) { CV_InpaintTest test; test.safe_run(); }

modules/optim/test/test_main.cpp

+#include "test_precomp.hpp"
+
+CV_TEST_MAIN("cv")

modules/optim/test/test_precomp.cpp

+#include "test_precomp.hpp"

modules/optim/test/test_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include <iostream>
+#include "opencv2/ts.hpp"
+#include "opencv2/photo.hpp"
+#include "opencv2/highgui.hpp"
+
+#endif