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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.

Abstract

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.

Spotlight Video

Video

Pre-trained Models

Datasets SRResCycGAN
NTIRE2020 RWSR Sensor noise (σ = 8)
NTIRE2020 RWSR JPEG compression (quality=30)
NTIRE2020 RWSR Unknown corruptions
AIM2020 RISR Real image corruptions

BibTeX

@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}
              }

Quick Test

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:

Dependencies

  • Python 3.7 (version >= 3.0)
  • PyTorch >= 1.0 (CUDA version >= 8.0 if installing with CUDA.)
  • Python packages: pip install numpy opencv-python

Train models

  • The SR training code is based on the SRResCGAN.

Test models

  1. Clone this github repository as the following commands:
git clone https://github.com/RaoUmer/SRResCycGAN
cd SRResCycGAN
cd srrescycgan_code_demo
  1. 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).
  2. Download the pretrained models from Pre-trained Models section. Place the models in ./srrescycgan_code_demo/trained_nets_x4.
  3. Run the test. You can config in the test_srrescycgan.py.
python test_srrescgan.py
  1. The results are in the ./srrescycgan_code_demo/sr_results_x4 folder.

SRResCycGAN Architecture

Overall Representative diagram

Quantitative Results

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.

The AIM2020 Real Image SR Challenge Results (x4)

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

Visual Results

DIV2K Validation-set (100 images)

Here are the SR resutls comparison of our method on the DIV2K validation-set images.

Real-Image SR Challenge dataset images (Track-3)

Validation-set

You can download all the SR resutls of our method on the AIM 2020 Real-Image SR validation-set from the Google Drive: SRResCycGAN.

Test-set

You can download all the SR resutls of our method on the AIM 2020 Real-Image SR test-set from the Google Drive: SRResCycGAN.