Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang and Zhezhu Jin
Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based and local attention strategy to balance the performance and computational overhead, which restricts employing large receptive fields to capture global information and establish long dependencies in the early layers. To further improve the efficiency of capturing global information, in this work, we propose SwinFIR to extend SwinIR by replacing Fast Fourier Convolution (FFC) components, which have the image-wide receptive field. We also revisit other advanced techniques, i.e., data augmentation, pre-training, and feature ensemble to improve the effect of image reconstruction. And our feature ensemble method enables the performance of the model to be considerably enhanced without increasing the training and testing time. We applied our algorithm on multiple popular large-scale benchmarks and achieved state-of-the-art performance comparing to the existing methods. For example, our SwinFIR achieves the PSNR of 32.83 dB on Manga109 dataset, which is 0.8 dB higher than the state-of-the-art SwinIR method, a significant improvement.
Install Pytorch first. Then,
pip install -r requirements.txt
python setup.py develop
Single Image Super Resolution
python inference/inference_swinfir.py
Stereo Image Super Resolution
python inference/inference_swinfirssr.py
SwinFIR and HATFIR for single image super resolution
SwinFIRSSR for stereo image super resolution
Single Image Super Resolution
SwinFIR_SRx4.yml
as an example):
python swinfir/test.py -opt options/test/SwinFIR/SwinFIR_SRx4.yml
Stereo Image Super Resolution
SwinFIRSSR_SSRx4.yml
as an example):
python swinfir/test.py -opt options/test/SwinFIRSSR/SwinFIRSSR_SSRx4.yml
./options/train
for the configuration file of the model to train.CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 swinfir/train.py -opt options/train/SwinFIR/train_SwinFIR_SRx2_from_scratch.yml --launcher pytorch
The training logs and weights will be saved in the ./experiments
folder.
Classical Image Super-Resolution
Method | Scale | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
---|---|---|---|---|---|---|
SwinIR | X2 | 38.42 | 34.46 | 32.53 | 33.81 | 39.92 |
SwinFIR | X2 | 38.67 | 34.94 | 32.66 | 34.59 | 40.63 |
HAT | X2 | 33.73 | 35.13 | 32.69 | 34.81 | 40.71 |
HATFIR | X2 | 38.77 | 35.19 | 32.73 | 34.97 | 40.78 |
SwinIR | X3 | 34.97 | 30.93 | 29.46 | 29.75 | 35.12 |
SwinFIR | X3 | 35.16 | 31.25 | 29.56 | 30.45 | 35.77 |
HAT | X3 | 35.16 | 31.33 | 29.59 | 30.70 | 35.84 |
HATFIR | X3 | 35.22 | 31.37 | 29.61 | 30.78 | 35.91 |
SwinIR | X4 | 32.92 | 29.09 | 27.92 | 27.45 | 32.03 |
SwinFIR | X4 | 33.20 | 29.36 | 28.03 | 28.14 | 32.84 |
HAT | X4 | 33.18 | 29.38 | 28.05 | 28.37 | 32.87 |
HATFIR | X4 | 33.29 | 29.47 | 28.08 | 28.44 | 33.03 |
Lightweight Image Super-Resolution
Method | Scale | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
---|---|---|---|---|---|---|
SwinIR | X2 | 38.14 | 33.86 | 32.31 | 32.76 | 39.12 |
SwinFIR | X2 | 38.30 | 34.28 | 32.43 | 33.33 | 39.71 |
SwinIR | X3 | 34.62 | 30.54 | 29.20 | 28.66 | 33.98 |
SwinFIR | X3 | 34.76 | 30.68 | 29.30 | 29.05 | 34.59 |
SwinIR | X4 | 32.44 | 28.77 | 27.69 | 26.47 | 30.92 |
SwinFIR | X4 | 32.67 | 28.99 | 27.80 | 26.99 | 31.68 |
Stereo Image Super-Resolution
Method | Scale | KITTI 2012 | KITTI 2015 | Middlebury | Flickr 1024 |
---|---|---|---|---|---|
NAFSSR-L | X2 | 31.60 | 31.25 | 35.88 | 29.68 |
SwinFIRSSR | X2 | 31.79 | 31.45 | 36.52 | 30.14 |
NAFSSR-L | X4 | 27.12 | 26.96 | 30.30 | 24.17 |
SwinFIRSSR | X4 | 27.16 | 26.89 | 30.44 | 24.30 |
If you find this project useful for your research, please consider citing:
@article{zhang2022swinfir,
title={Swinfir: Revisiting the swinir with fast fourier convolution and improved training for image super-resolution},
author={Zhang, Dafeng and Huang, Feiyu and Liu, Shizhuo and Wang, Xiaobing and Jin, Zhezhu},
journal={arXiv preprint arXiv:2208.11247},
year={2022}
}