File size: 29,447 Bytes
3f26a5d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 |
# SwinIR: Image Restoration Using Swin Transformer
[Jingyun Liang](https://jingyunliang.github.io), [Jiezhang Cao](https://www.jiezhangcao.com/), [Guolei Sun](https://vision.ee.ethz.ch/people-details.MjYzMjMw.TGlzdC8zMjg5LC0xOTcxNDY1MTc4.html), [Kai Zhang](https://cszn.github.io/), [Luc Van Gool](https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en), [Radu Timofte](http://people.ee.ethz.ch/~timofter/)
Computer Vision Lab, ETH Zurich
---
[](https://arxiv.org/abs/2108.10257)
[](https://github.com/JingyunLiang/SwinIR)
[](https://github.com/JingyunLiang/SwinIR/releases)
[ <a href="https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb)
<a href="https://replicate.ai/jingyunliang/swinir"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=blue"></a>
[](https://playtorch.dev/snack/@playtorch/swinir/)
[Gradio Web Demo](https://huggingface.co/spaces/akhaliq/SwinIR)
This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer
([arxiv](https://arxiv.org/pdf/2108.10257.pdf), [supp](https://github.com/JingyunLiang/SwinIR/releases), [pretrained models](https://github.com/JingyunLiang/SwinIR/releases), [visual results](https://github.com/JingyunLiang/SwinIR/releases)). SwinIR achieves **state-of-the-art performance** in
- bicubic/lighweight/real-world image SR
- grayscale/color image denoising
- grayscale/color JPEG compression artifact reduction
</br>
:rocket: :rocket: :rocket: **News**:
- **Aug. 16, 2022**: Add PlayTorch Demo on running the real-world image SR model on mobile devices [](https://playtorch.dev/snack/@playtorch/swinir/).
- **Aug. 01, 2022**: Add pretrained models and results on JPEG compression artifact reduction for color images.
- **Jun. 10, 2022**: See our work on video restoration :fire::fire::fire: [VRT: A Video Restoration Transformer](https://github.com/JingyunLiang/VRT)
[](https://github.com/JingyunLiang/VRT)
[](https://github.com/JingyunLiang/VRT/releases)
and [RVRT: Recurrent Video Restoration Transformer](https://github.com/JingyunLiang/RVRT)
[](https://github.com/JingyunLiang/RVRT)
[](https://github.com/JingyunLiang/RVRT/releases)
for video SR, video deblurring, video denoising, video frame interpolation and space-time video SR.
- **Sep. 07, 2021**: We provide an interactive online Colab demo for real-world image SR <a href="https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>:fire: for comparison with [the first practical degradation model BSRGAN (ICCV2021) ](https://github.com/cszn/BSRGAN) and a recent model RealESRGAN. Try to super-resolve your own images on Colab!
|Real-World Image (x4)|[BSRGAN, ICCV2021](https://github.com/cszn/BSRGAN)|[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)|SwinIR (ours)|SwinIR-Large (ours)|
| :--- | :---: | :-----: | :-----: | :-----: |
| <img width="200" src="figs/ETH_LR.png">|<img width="200" src="figs/ETH_BSRGAN.png">|<img width="200" src="figs/ETH_realESRGAN.jpg">|<img width="200" src="figs/ETH_SwinIR.png">|<img width="200" src="figs/ETH_SwinIR-L.png">
|<img width="200" src="figs/OST_009_crop_LR.png">|<img width="200" src="figs/OST_009_crop_BSRGAN.png">|<img width="200" src="figs/OST_009_crop_realESRGAN.png">|<img width="200" src="figs/OST_009_crop_SwinIR.png">|<img width="200" src="figs/OST_009_crop_SwinIR-L.png">|
- ***Aug. 26, 2021**: See our recent work on [real-world image SR: a pratical degrdation model BSRGAN, ICCV2021](https://github.com/cszn/BSRGAN)
[](https://github.com/cszn/BSRGAN)*
- ***Aug. 26, 2021**: See our recent work on [generative modelling of image SR and image rescaling: normalizing-flow-based HCFlow, ICCV2021](https://github.com/JingyunLiang/HCFlow)
[](https://github.com/JingyunLiang/HCFlow)[ <a href="https://colab.research.google.com/gist/JingyunLiang/cdb3fef89ebd174eaa43794accb6f59d/hcflow-demo-on-x8-face-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/cdb3fef89ebd174eaa43794accb6f59d/hcflow-demo-on-x8-face-image-sr.ipynb)*
- ***Aug. 26, 2021**: See our recent work on [blind SR: spatially variant kernel estimation (MANet, ICCV2021)](https://github.com/JingyunLiang/MANet) [](https://github.com/JingyunLiang/MANet)
[ <a href="https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb) and [unsupervised kernel estimation (FKP, CVPR2021)](https://github.com/JingyunLiang/FKP)
[](https://github.com/JingyunLiang/FKP)*
---
> Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.
><p align="center">
<img width="800" src="figs/SwinIR_archi.png">
</p>
#### Contents
1. [Training](#Training)
1. [Testing](#Testing)
1. [Results](#Results)
1. [Citation](#Citation)
1. [License and Acknowledgement](#License-and-Acknowledgement)
### Training
Used training and testing sets can be downloaded as follows:
| Task | Training Set | Testing Set| Visual Results |
|:----------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| :---: | :---: |
| classical/lightweight image SR | [DIV2K](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar) (800 training images) or DIV2K +[Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (2650 images) | Set5 + Set14 + BSD100 + Urban100 + Manga109 [download all](https://drive.google.com/drive/folders/1B3DJGQKB6eNdwuQIhdskA64qUuVKLZ9u) | [here](https://github.com/JingyunLiang/SwinIR/releases) |
| real-world image SR | SwinIR-M (middle size): [DIV2K](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar) (800 training images) +[Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (2650 images) + [OST](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip) ([alternative link](https://drive.google.com/drive/folders/1iZfzAxAwOpeutz27HC56_y5RNqnsPPKr), 10324 images for sky,water,grass,mountain,building,plant,animal) <br /> SwinIR-L (large size): DIV2K + Flickr2K + OST + [WED](http://ivc.uwaterloo.ca/database/WaterlooExploration/exploration_database_and_code.rar)(4744 images) + [FFHQ](https://drive.google.com/drive/folders/1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL) (first 2000 images, face) + Manga109 (manga) + [SCUT-CTW1500](https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip) (first 100 training images, texts) <br /><br /> ***We use the pionnerring practical degradation model from [BSRGAN, ICCV2021 ](https://github.com/cszn/BSRGAN)** | [RealSRSet+5images](https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/RealSRSet+5images.zip) | [here](https://github.com/JingyunLiang/SwinIR/releases) |
| color/grayscale image denoising | [DIV2K](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar) (800 training images) + [Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (2650 images) + [BSD500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz) (400 training&testing images) + [WED](http://ivc.uwaterloo.ca/database/WaterlooExploration/exploration_database_and_code.rar)(4744 images) <br /><br /> *BSD68/BSD100 images are not used in training. | grayscale: Set12 + BSD68 + Urban100 <br /> color: CBSD68 + Kodak24 + McMaster + Urban100 [download all](https://github.com/cszn/FFDNet/tree/master/testsets) | [here](https://github.com/JingyunLiang/SwinIR/releases) |
| grayscale/color JPEG compression artifact reduction | [DIV2K](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar) (800 training images) + [Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (2650 images) + [BSD500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz) (400 training&testing images) + [WED](http://ivc.uwaterloo.ca/database/WaterlooExploration/exploration_database_and_code.rar)(4744 images) | grayscale: Classic5 +LIVE1 [download all](https://github.com/cszn/DnCNN/tree/master/testsets) | [here](https://github.com/JingyunLiang/SwinIR/releases) |
<!--
| Task | Training Set | Testing Set| Pretrained Model and Visual Results of SwinIR |
| :--- | :---: | :---: |:---: |
| image denoising (real) | [SIDD-Medium-sRGB](https://www.eecs.yorku.ca/~kamel/sidd/dataset.php) (320 images, [preprocess]()) + [RENOIR](http://ani.stat.fsu.edu/~abarbu/Renoir.html) (221 images, [preprocess](https://github.com/zsyOAOA/DANet/blob/master/datasets/preparedata/Renoir_big2small_all.py)) + [Poly](https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset) (40 images in ./OriginalImages) | [SIDD validation set](https://drive.google.com/drive/folders/1S44fHXaVxAYW3KLNxK41NYCnyX9S79su) (1280 patches, identical to official [.mat](https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php) version) + [DND](https://noise.visinf.tu-darmstadt.de/downloads/) (pre-defined 100 patches of 50 images, [online eval](https://noise.visinf.tu-darmstadt.de/submit/)) + [Nam](https://www.dropbox.com/s/24kds7c436i5i11/real_image_noise_dataset.zip?dl=0) (random 100 patches of 17 images, [preprocess](https://github.com/zsyOAOA/DANet/blob/master/datasets/preparedata/Nam_patch_prepare.py))|[download model]() [download results]() |
| image deblurring (synthetic) | [GoPro](https://drive.google.com/drive/folders/1AsgIP9_X0bg0olu2-1N6karm2x15cJWE) (2103 training images) | [GoPro](https://drive.google.com/drive/folders/1a2qKfXWpNuTGOm2-Jex8kfNSzYJLbqkf) (1111 images) + [HIDE](https://drive.google.com/drive/folders/1nRsTXj4iTUkTvBhTcGg8cySK8nd3vlhK) (2050 images) + [RealBlur_J](https://drive.google.com/drive/folders/1KYtzeKCiDRX9DSvC-upHrCqvC4sPAiJ1) (real blur, 980 images) + [RealBlur_R](https://drive.google.com/drive/folders/1EwDoajf5nStPIAcU4s9rdc8SPzfm3tW1) (real blur, 980 images) | [download model]() [download results]()|
| image deraining (synthetic) | [Multiple datasets](https://drive.google.com/drive/folders/1Hnnlc5kI0v9_BtfMytC2LR5VpLAFZtVe) (13711 training images, see Table 1 of [MPRNet](https://github.com/swz30/MPRNet) for details.) | Rain100H (100 images) + Rain100L (100 images) + Test100 (100 images) + Test2800 (2800 images) + Test1200 (1200 images), [download all](https://drive.google.com/drive/folders/1PDWggNh8ylevFmrjo-JEvlmqsDlWWvZs) | [download model]() [download results]()|
Note: above datasets may come from the official release or some awesome collections ([BasicSR](https://github.com/xinntao/BasicSR), [MPRNet](https://github.com/swz30/MPRNet)).
-->
The training code is at [KAIR](https://github.com/cszn/KAIR/blob/master/docs/README_SwinIR.md).
## Testing (without preparing datasets)
For your convience, we provide some example datasets (~20Mb) in `/testsets`.
If you just want codes, downloading `models/network_swinir.py`, `utils/util_calculate_psnr_ssim.py` and `main_test_swinir.py` is enough.
Following commands will download [pretrained models](https://github.com/JingyunLiang/SwinIR/releases) **automatically** and put them in `model_zoo/swinir`.
**[All visual results of SwinIR can be downloaded here](https://github.com/JingyunLiang/SwinIR/releases)**.
We also provide an [online Colab demo for real-world image SR <a href="https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb) for comparison with [the first practical degradation model BSRGAN (ICCV2021) ](https://github.com/cszn/BSRGAN) and a recent model [RealESRGAN](https://github.com/xinntao/Real-ESRGAN). Try to test your own images on Colab!
We provide a PlayTorch demo [](https://playtorch.dev/snack/@playtorch/swinir/) for real-world image SR to showcase how to run the SwinIR model in mobile application built with React Native.
```bash
# 001 Classical Image Super-Resolution (middle size)
# Note that --training_patch_size is just used to differentiate two different settings in Table 2 of the paper. Images are NOT tested patch by patch.
# (setting1: when model is trained on DIV2K and with training_patch_size=48)
python main_test_swinir.py --task classical_sr --scale 2 --training_patch_size 48 --model_path model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.pth --folder_lq testsets/Set5/LR_bicubic/X2 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 3 --training_patch_size 48 --model_path model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x3.pth --folder_lq testsets/Set5/LR_bicubic/X3 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 4 --training_patch_size 48 --model_path model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth --folder_lq testsets/Set5/LR_bicubic/X4 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 8 --training_patch_size 48 --model_path model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x8.pth --folder_lq testsets/Set5/LR_bicubic/X8 --folder_gt testsets/Set5/HR
# (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
python main_test_swinir.py --task classical_sr --scale 2 --training_patch_size 64 --model_path model_zoo/swinir/001_classicalSR_DF2K_s64w8_SwinIR-M_x2.pth --folder_lq testsets/Set5/LR_bicubic/X2 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 3 --training_patch_size 64 --model_path model_zoo/swinir/001_classicalSR_DF2K_s64w8_SwinIR-M_x3.pth --folder_lq testsets/Set5/LR_bicubic/X3 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 4 --training_patch_size 64 --model_path model_zoo/swinir/001_classicalSR_DF2K_s64w8_SwinIR-M_x4.pth --folder_lq testsets/Set5/LR_bicubic/X4 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task classical_sr --scale 8 --training_patch_size 64 --model_path model_zoo/swinir/001_classicalSR_DF2K_s64w8_SwinIR-M_x8.pth --folder_lq testsets/Set5/LR_bicubic/X8 --folder_gt testsets/Set5/HR
# 002 Lightweight Image Super-Resolution (small size)
python main_test_swinir.py --task lightweight_sr --scale 2 --model_path model_zoo/swinir/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x2.pth --folder_lq testsets/Set5/LR_bicubic/X2 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task lightweight_sr --scale 3 --model_path model_zoo/swinir/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x3.pth --folder_lq testsets/Set5/LR_bicubic/X3 --folder_gt testsets/Set5/HR
python main_test_swinir.py --task lightweight_sr --scale 4 --model_path model_zoo/swinir/002_lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth --folder_lq testsets/Set5/LR_bicubic/X4 --folder_gt testsets/Set5/HR
# 003 Real-World Image Super-Resolution (use --tile 400 if you run out-of-memory)
# (middle size)
python main_test_swinir.py --task real_sr --scale 4 --model_path model_zoo/swinir/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth --folder_lq testsets/RealSRSet+5images --tile
# (larger size + trained on more datasets)
python main_test_swinir.py --task real_sr --scale 4 --large_model --model_path model_zoo/swinir/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth --folder_lq testsets/RealSRSet+5images
# 004 Grayscale Image Deoising (middle size)
python main_test_swinir.py --task gray_dn --noise 15 --model_path model_zoo/swinir/004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth --folder_gt testsets/Set12
python main_test_swinir.py --task gray_dn --noise 25 --model_path model_zoo/swinir/004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth --folder_gt testsets/Set12
python main_test_swinir.py --task gray_dn --noise 50 --model_path model_zoo/swinir/004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth --folder_gt testsets/Set12
# 005 Color Image Deoising (middle size)
python main_test_swinir.py --task color_dn --noise 15 --model_path model_zoo/swinir/005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth --folder_gt testsets/McMaster
python main_test_swinir.py --task color_dn --noise 25 --model_path model_zoo/swinir/005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth --folder_gt testsets/McMaster
python main_test_swinir.py --task color_dn --noise 50 --model_path model_zoo/swinir/005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth --folder_gt testsets/McMaster
# 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)
# grayscale
python main_test_swinir.py --task jpeg_car --jpeg 10 --model_path model_zoo/swinir/006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth --folder_gt testsets/classic5
python main_test_swinir.py --task jpeg_car --jpeg 20 --model_path model_zoo/swinir/006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth --folder_gt testsets/classic5
python main_test_swinir.py --task jpeg_car --jpeg 30 --model_path model_zoo/swinir/006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth --folder_gt testsets/classic5
python main_test_swinir.py --task jpeg_car --jpeg 40 --model_path model_zoo/swinir/006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth --folder_gt testsets/classic5
# color
python main_test_swinir.py --task color_jpeg_car --jpeg 10 --model_path model_zoo/swinir/006_colorCAR_DFWB_s126w7_SwinIR-M_jpeg10.pth --folder_gt testsets/LIVE1
python main_test_swinir.py --task color_jpeg_car --jpeg 20 --model_path model_zoo/swinir/006_colorCAR_DFWB_s126w7_SwinIR-M_jpeg20.pth --folder_gt testsets/LIVE1
python main_test_swinir.py --task color_jpeg_car --jpeg 30 --model_path model_zoo/swinir/006_colorCAR_DFWB_s126w7_SwinIR-M_jpeg30.pth --folder_gt testsets/LIVE1
python main_test_swinir.py --task color_jpeg_car --jpeg 40 --model_path model_zoo/swinir/006_colorCAR_DFWB_s126w7_SwinIR-M_jpeg40.pth --folder_gt testsets/LIVE1
```
---
## Results
We achieved state-of-the-art performance on classical/lightweight/real-world image SR, grayscale/color image denoising and JPEG compression artifact reduction. Detailed results can be found in the [paper](https://arxiv.org/abs/2108.10257). All visual results of SwinIR can be downloaded [here](https://github.com/JingyunLiang/SwinIR/releases).
<details>
<summary>Classical Image Super-Resolution (click me)</summary>
<p align="center">
<img width="900" src="figs/classic_image_sr.png">
<img width="900" src="figs/classic_image_sr_visual.png">
</p>
- More detailed comparison between SwinIR and a representative CNN-based model RCAN (classical image SR, X4)
| Method | Training Set | Training time <br /> (8GeForceRTX2080Ti <br /> batch=32, iter=500k) |Y-PSNR/Y-SSIM <br /> on Manga109 | Run time <br /> (1GeForceRTX2080Ti,<br /> on 256x256 LR image)* | #Params | #FLOPs | Testing memory |
| :--- | :---: | :-----: | :---: | :---: | :---: | :---: | :---: |
| RCAN | DIV2K | 1.6 days | 31.22/0.9173 | 0.180s | 15.6M | 850.6G | 593.1M |
| SwinIR | DIV2K | 1.8 days |31.67/0.9226 | 0.539s | 11.9M | 788.6G | 986.8M |
\* We re-test the runtime when the GPU is idle. We refer to the evluation code [here](https://github.com/cszn/KAIR/blob/master/main_challenge_sr.py).
- Results on DIV2K-validation (100 images)
| Training Set | scale factor | PSNR (RGB) | PSNR (Y) | SSIM (RGB) | SSIM (Y) |
| :--- | :---: | :---: | :---: | :---: | :---: |
| DIV2K (800 images) | 2 | 35.25 | 36.77 | 0.9423 | 0.9500 |
| DIV2K+Flickr2K (2650 images) | 2 | 35.34 | 36.86 | 0.9430 |0.9507 |
| DIV2K (800 images) | 3 | 31.50 | 32.97 | 0.8832 |0.8965 |
| DIV2K+Flickr2K (2650 images) | 3 | 31.63 | 33.10 | 0.8854 |0.8985 |
| DIV2K (800 images) | 4 | 29.48 | 30.94 | 0.8311|0.8492 |
| DIV2K+Flickr2K (2650 images) | 4 | 29.63 | 31.08 | 0.8347|0.8523 |
</details>
<details>
<summary>Lightweight Image Super-Resolution</summary>
<p align="center">
<img width="900" src="figs/lightweight_image_sr.png">
</p>
</details>
<details>
<summary>Real-World Image Super-Resolution</summary>
<p align="center">
<img width="900" src="figs/real_world_image_sr.png">
</p>
</details>
<details>
<summary>Grayscale Image Deoising</summary>
<p align="center">
<img width="900" src="figs/gray_image_denoising.png">
</p>
</details>
<details>
<summary>Color Image Deoising</summary>
<p align="center">
<img width="900" src="figs/color_image_denoising.png">
</p>
</details>
<details>
<summary>JPEG Compression Artifact Reduction</summary>
on grayscale images
<p align="center">
<img width="900" src="figs/jepg_compress_artfact_reduction.png">
</p>
on color images
| Training Set | quality factor | PSNR (RGB) | PSNR-B (RGB) | SSIM (RGB) |
|:-------------|:--------------:|:----------:|:------------:|:----------:|
| LIVE1 | 10 | 28.06 | 27.76 | 0.8089 |
| LIVE1 | 20 | 30.45 | 29.97 | 0.8741 |
| LIVE1 | 30 | 31.82 | 31.24 | 0.9018 |
| LIVE1 | 40 | 32.75 | 32.12 | 0.9174 |
</details>
## Citation
@article{liang2021swinir,
title={SwinIR: Image Restoration Using Swin Transformer},
author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
journal={arXiv preprint arXiv:2108.10257},
year={2021}
}
## License and Acknowledgement
This project is released under the Apache 2.0 license. The codes are based on [Swin Transformer](https://github.com/microsoft/Swin-Transformer) and [KAIR](https://github.com/cszn/KAIR). Please also follow their licenses. Thanks for their awesome works. |