Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution (SRResCycGAN)
An official PyTorch implementation of the SRResCycGAN network as described in the paper Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution. This work is participated in the AIM 2020 Real-Image Super-resolution challenge track-3 at the high x4 upscaling factor.
Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic down-sampling assumption. However, such degradation process is not available in real-world settings. We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation in an end-to-end manner. We demonstrate our proposed approach in the quantitative and qualitative experiments that generalize well to the real image super-resolution and it is easy to deploy for the mobile/embedded devices. In addition, our SR results on the AIM 2020 Real Image SR Challenge datasets demonstrate that the proposed SR approach achieves comparable results as the other state-of-art methods.
Datasets | SRResCycGAN |
---|---|
NTIRE2020 RWSR | Sensor noise (σ = 8) |
NTIRE2020 RWSR | JPEG compression (quality=30) |
NTIRE2020 RWSR | Unknown corruptions |
AIM2020 RISR | Real image corruptions |
@InProceedings{Umer_2020_ECCVW,
author = {Muhammad Umer, Rao and Micheloni, Christian},
title = {Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {August},
year = {2020}
}
This model can be run on arbitrary images with a Docker image hosted on Replicate: https://beta.replicate.ai/RaoUmer/SRResCycGAN. Below are instructions for how to run the model without Docker:
- Python 3.7 (version >= 3.0)
- PyTorch >= 1.0 (CUDA version >= 8.0 if installing with CUDA.)
- Python packages:
pip install numpy opencv-python
- The SR training code is based on the SRResCGAN.
- Clone this github repository as the following commands:
git clone https://github.com/RaoUmer/SRResCycGAN
cd SRResCycGAN
cd srrescycgan_code_demo
- Place your own low-resolution images in the
./srrescycgan_code_demo/LR
folder. (There are two sample images i.e. LR_006 and LR_014). - Download the pretrained models from Pre-trained Models section. Place the models in
./srrescycgan_code_demo/trained_nets_x4
. - Run the test. You can config in the
test_srrescycgan.py
.
python test_srrescgan.py
- The results are in the
./srrescycgan_code_demo/sr_results_x4
folder.
The x4 SR quantitative results comparison of our method with others over the DIV2K validation-set (100 images). The best performance is shown in red and the second best performance is shown in blue.
Team | PSNR↑ | SSIM↑ | Weighed_score↑ |
---|---|---|---|
Baidu | 31.3960 | 0.8751 | 0.7099 (1) |
ALONG | 31.2369 | 0.8742 | 0.7076 (2) |
CETC-CSKT | 31.1226 | 0.8744 | 0.7066 (3) |
SR-IM | 31.2369 | 0.8728 | 0.7057 |
DeepBlueAI | 30.9638 | 0.8737 | 0.7044 |
JNSR | 30.9988 | 0.8722 | 0.7035 |
OPPO_CAMERA | 30.8603 | 0.8736 | 0.7033 |
Kailos | 30.8659 | 0.8734 | 0.7031 |
SR_DL | 30.6045 | 0.8660 | 0.6944 |
Noah_TerminalVision | 30.5870 | 0.8662 | 0.6944 |
Webbzhou | 30.4174 | 0.8673 | 0.6936 |
TeamInception | 30.3465 | 0.8681 | 0.6935 |
IyI | 30.3191 | 0.8655 | 0.6911 |
MCML-Yonsei | 30.4201 | 0.8637 | 0.6906 |
MoonCloud | 30.2827 | 0.8644 | 0.6898 |
qwq | 29.5878 | 0.8547 | 0.6748 |
SrDance | 29.5952 | 0.8523 | 0.6729 |
MLP_SR (ours) | 28.6185 | 0.8314 | 0.6457 |
EDSR | 28.2120 | 0.8240 | 0.6356 |
RRDN_IITKGP | 27.9708 | 0.8085 | 0.6201 |
congxiaofeng | 26.3915 | 0.8258 | 0.6187 |
Here are the SR resutls comparison of our method on the DIV2K validation-set images.
Real-Image SR Challenge dataset images (Track-3)
You can download all the SR resutls of our method on the AIM 2020 Real-Image SR validation-set from the Google Drive: SRResCycGAN.
You can download all the SR resutls of our method on the AIM 2020 Real-Image SR test-set from the Google Drive: SRResCycGAN.