## NAFSSR: Stereo Image Super-Resolution Using NAFNet
The official pytorch implementation of the paper **[NAFSSR: Stereo Image Super-Resolution Using NAFNet]()**
#### Xiaojie Chu\*, Liangyu Chen\*, Wenqing Yu
>This paper proposes a simple baseline named NAFSSR for stereo image super-resolution. We use a stack of NAFNet's Block (NAFBlock) for intra-view feature extraction and combine it with Stereo Cross Attention Modules (SCAM) for cross-view feature interaction.
>NAFSSR outperforms the state-of-the-art methods on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won **1st place** in the [NTIRE 2022 Stereo Image Super-resolution Challenge](https://codalab.lisn.upsaclay.fr/competitions/1598).
# Reproduce the Stereo SR Results
## 1. Data Preparation
Follow previous works, our models are trained with Flickr1024 and Middlebury datasets, which is exactly the same as iPASSR. Please visit their homepage and follow their instructions to download and prepare the datasets.
#### Download and prepare the train set and place it in ```./datasets/StereoSR```
#### Download and prepare the evaluation data and place it in ```./datasets/StereoSR/test```
The structure of `datasets` directory should be like
```
datasets
├── StereoSR
│ ├── patches_x2
│ │ ├── 000001
│ │ ├── 000002
│ │ ├── ...
│ │ ├── 298142
│ │ └── 298143
│ ├── patches_x4
│ │ ├── 000001
│ │ ├── 000002
│ │ ├── ...
│ │ ├── 049019
│ │ └── 049020
| ├── test
│ | ├── Flickr1024
│ │ │ ├── hr
│ │ │ ├── lr_x2
│ │ │ └── lr_x4
│ | ├── KITTI2012
│ │ │ ├── hr
│ │ │ ├── lr_x2
│ │ │ └── lr_x4
│ | ├── KITTI2015
│ │ │ ├── hr
│ │ │ ├── lr_x2
│ │ │ └── lr_x4
│ │ └── Middlebury
│ │ ├── hr
│ │ ├── lr_x2
│ │ └── lr_x4
```
## 2. Evaluation
#### Download the pretrain model in ```./experiments/pretrained_models/```
| name | scale |#Params|PSNR|SSIM| pretrained models | configs |
|:----:|:----:|:----:|:----:|:----:|:----:|-----:|
|NAFSSR-T|x4|0.46M|23.69|0.7384|[gdrive](https://drive.google.com/file/d/1owfYG1KTXFMl4wHpUZefWAcVlBpLohe5/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1yC5XzJcL5peC1YuW3MkFMA?pwd=5j1u)|[train](../options/test/NAFSSR/NAFSSR-T_4x.yml) \| [test](../options/test/NAFSSR/NAFSSR-T_4x.yml)|
|NAFSSR-S|x4|1.56M|23.88|0.7468|[gdrive](https://drive.google.com/file/d/1RpfS2lemsgetIQwBwkZpZwLBJfOTDCU5/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1XvwM5KVhNsKAxWbxU85SFA?pwd=n5au)|[train](../options/test/NAFSSR/NAFSSR-S_4x.yml) \| [test](../options/test/NAFSSR/NAFSSR-S_4x.yml)|
|NAFSSR-B|x4|6.80M|24.07|0.7551|[gdrive](https://drive.google.com/file/d/1Su0OTp66_NsXUbqTAIi1msvsp0G5WVxp/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/18tVlH-QIVtvDC1LM2oPatw?pwd=3up5)|[train](../options/test/NAFSSR/NAFSSR-B_4x.yml) \| [test](../options/test/NAFSSR/NAFSSR-B_4x.yml)|
|NAFSSR-L|x4|23.83M|24.17|0.7589|[gdrive](https://drive.google.com/file/d/1TIdQhPtBrZb2wrBdAp9l8NHINLeExOwb/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1P8ioEuI1gwydA2Avr3nUvw?pwd=qs7a)|[train](../options/test/NAFSSR/NAFSSR-L_4x.yml) \| [test](../options/test/NAFSSR/NAFSSR-L_4x.yml)|
|NAFSSR-T|x2|0.46M|28.94|0.9128|[gdrive](https://drive.google.com/file/d/1sBivtt5KaFMjMhBwyajYy1uemIEFQBvW/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1RDW923v0e0G_eYvTF8I7Dg?pwd=utgs)|[train](../options/test/NAFSSR/NAFSSR-T_2x.yml) \| [test](../options/test/NAFSSR/NAFSSR-T_2x.yml)|
|NAFSSR-S|x2|1.56M|29.19|0.9160|[gdrive](https://drive.google.com/file/d/1caVrp3fFSpwiU8RPGXe-zDD1tU110yxA/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1qJmQzv-YV1V9raim57pTIQ?pwd=t2or)|[train](../options/test/NAFSSR/NAFSSR-S_2x.yml) \| [test](../options/test/NAFSSR/NAFSSR-S_2x.yml)|
|NAFSSR-B|x2|6.80M|29.54|0.7551|[gdrive](https://drive.google.com/file/d/1gOfDTfyCaff_xNm86u8sN_3MtAZvnQEk/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1IkadqW1uWx9xM5K2ETJ9-A?pwd=pv1f)|[train](../options/test/NAFSSR/NAFSSR-B_2x.yml) \| [test](../options/test/NAFSSR/NAFSSR-B_2x.yml)|
|NAFSSR-L|x2|23.79M|29.68|0.9221|[gdrive](https://drive.google.com/file/d/1SZ6bQVYTVS_AXedBEr-_mBCC-qGYHLmf/view?usp=sharing) \| [baidu](https://pan.baidu.com/s/1GS6YQSSECH8hAKhvzw6GyQ?pwd=2v3v)|[train](../options/test/NAFSSR/NAFSSR-L_2x.yml) \| [test](../options/test/NAFSSR/NAFSSR-L_2x.yml)|
*PSNR/SSIM are evaluate on Flickr1024 test set.*
### Testing on KITTI2012, KITTI2015, Middlebury, Flickr1024 datasets
* NAFSSR-T for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-T_x4.yml --launcher pytorch
```
* NAFSSR-S for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-S_x4.yml --launcher pytorch
```
* NAFSSR-B for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-B_x4.yml --launcher pytorch
```
* NAFSSR-L for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-L_x4.yml --launcher pytorch
```
* NAFSSR-L for 2x SR:
```
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-L_x2.yml --launcher pytorch
```
* Test by a single gpu by default. Set ```--nproc_per_node``` to # of gpus for distributed validation.
## 3. Training
* NAFNet-B for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/NAFSSR/NAFSSR-B_x4.yml --launcher pytorch
```
* NAFNet-S for 4x SR:
```
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/NAFSSR/NAFSSR-S_x4.yml --launcher pytorch
```
* 8 gpus by default. Set ```--nproc_per_node``` to # of gpus for distributed validation.