Video super-resolution originates from image super-resolution, which aims to recover high-resolution (HR) images from one or more low resolution (LR) images. The difference between them is that the video is composed of multiple frames, so the video super-resolution usually uses the information between frames to repair. Here we provide the video super-resolution model [EDVR](https://arxiv.org/pdf/1905.02716.pdf).
[EDVR](https://arxiv.org/pdf/1905.02716.pdf) wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. The main difficulties of video super-resolution from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. First, to handle large motions, EDVR devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, EDVR propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.
## 1.2 How to use
### 1.2.1 Prepare Datasets
REDS（[download](https://seungjunnah.github.io/Datasets/reds.html)）is a newly proposed high-quality (720p) video dataset in the NTIRE19 Competition. REDS consists of 240 training clips, 30 validation clips and 30 testing clips (each with 100 consecutive frames). Since the test ground truth is not available, we select four representative clips (they are '000', '011', '015', '020', with diverse scenes and motions) as our test set, denoted by REDS4. The remaining training and validation clips are re-grouped as our training dataset (a total of 266 clips).
The structure of the processed REDS is as follows:
```
PaddleGAN
├── data
├── REDS
├── train_sharp
| └──X4
├── train_sharp_bicubic
| └──X4
├── REDS4_test_sharp
| └──X4
└── REDS4_test_sharp_bicubic
└──X4
...
```
### 1.2.2 Train/Test
The command to train and test edvr model with the processed EDVR is as follows:
