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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

Pre-trained Models

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

2. 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).
3. Download the pretrained models from Pre-trained Models section. Place the models in ./srrescycgan_code_demo/trained_nets_x4.
4. Run the test. You can config in the test_srrescycgan.py.

python test_srrescgan.py

5. 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)

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.