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NAFSSR: Stereo Image Super-Resolution Using NAFNet

The official pytorch implementation of the paper NAFSSR: Stereo Image Super-Resolution Using NAFNet. You can get more infomation about NAFSSR with folloing links: [video]/[slides]/[poster].

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.

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 \| baidu	train \| test
NAFSSR-S	x4	1.56M	23.88	0.7468	gdrive \| baidu	train \| test
NAFSSR-B	x4	6.80M	24.07	0.7551	gdrive \| baidu	train \| test
NAFSSR-L	x4	23.83M	24.17	0.7589	gdrive \| baidu	train \| test
NAFSSR-T	x2	0.46M	28.94	0.9128	gdrive \| baidu	train \| test
NAFSSR-S	x2	1.56M	29.19	0.9160	gdrive \| baidu	train \| test
NAFSSR-B	x2	6.80M	29.54	0.7551	gdrive \| baidu	train \| test
NAFSSR-L	x2	23.79M	29.68	0.9221	gdrive \| baidu	train \| test

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.

Citations

If NAFSSR helps your research or work, please consider citing NAFSSR.

@InProceedings{chu2022nafssr,
    author    = {Chu, Xiaojie and Chen, Liangyu and Yu, Wenqing},
    title     = {NAFSSR: Stereo Image Super-Resolution Using NAFNet},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2022},
    pages     = {1239-1248}
}

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