SAN_pytorch

Second-order Attention Network for Single Image Super-resolution

Pre-trained Models

Data

All models trained on the same anime-themed datased based on Danbooru2019. Final dataset consists of 1000/100 (train/val) images. Original images are all PNG images at least 2K x 2K. Images downsampled to 2K by LANCZOS, that is they have 2K pixels on at least one of the axes (vertical or horizontal), and then cropped to multiple of 12 pixels on both axes. All images splitted into 96x96/192x192 (x2/x4) HR and 48x48 LR (with jpeg noise) overlapping patches. All HR patches filtered by it's gradient and variance, and stored in SQLite database.

Image noise are from JPEG format only. Same as for waifu2x. Noise level 1 means quality ranges uniformly from [75, 95]; level 2 means quality ranges uniformly from [50, 75].

Note: Dataset may contain some NSFW data.

Anime - Scale factor x2 - Noise level 0 - train / val

Anime - Scale factor x2 - Noise level 1 - train / val

Anime - Scale factor x4 - Noise level 0 - train / val

Anime - Scale factor x4 - Noise level 1 - train / val

Scores

Scores calculated on validation dataset which consists of ~14K HR/LR patches for scale factor of 2.

Model	Scale factor	Noise level	PSNR(+)	SSIM(+)
SAN	2	0	43.7363	0.9965
SAN	2	1	37.1034	0.9866
SAN	4	0	34.6585	0.9790
SAN	4	1	29.2292	0.9354

Models

SAN - Scale factor x2 - Noise level 0

SAN - Scale factor x2 - Noise level 1

SAN - Scale factor x4 - Noise level 0

SAN - Scale factor x4 - Noise level 1

Usage

Note: SAN is scale-depedent model so you need to use corresponding model for specified scale factor. Using x2 model to do a x3/x4/x8 upscale produces VERY noisy samples.

python test.py --scale 2 --checkpoint path/to/model.pth --image path/to/image.jpg --output SR_output_x2.png

Citation

@InProceedings{Dai_2019_CVPR,
    author = {Dai, Tao and Cai, Jianrui and Zhang, Yongbing and Xia, Shu-Tao and Zhang, Lei},
    title = {Second-Order Attention Network for Single Image Super-Resolution},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2019}
}

Acknowledgments

Model codes mainly based on reference implementation.

ImageSplitter and SQLite-based dataset are based on yu45020's waifu2x re-implementation.

SQLite-based data generator based on NatSR implementation.

Yukariin/SAN_pytorch