raoumer / srrescycgan

Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

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Intelligent image scaling to 4x resolution.

Examples

Input Output

Usage

Given that you have a folder of low-resolution images in the folder ./input, the following command saves high-resolution results to the folder ./output.

Run on GPU

This model requires an NVIDIA GPU, compatible with CUDA 11.0.

docker run --gpus=all -it \
    -v $PWD/srrescycgan_code_demo/samples:/input \
    -v $PWD/output:/output \
    us-docker.pkg.dev/replicate/raoumer/srrescycgan:gpu \
    --input-folder=/input \
    --output-folder=/output

Run on CPU

docker run -it \
    -v $PWD/srrescycgan_code_demo/samples:/input \
    -v $PWD/output:/output \
    us-docker.pkg.dev/replicate/raoumer/srrescycgan:cpu \
    --input-folder=/input \
    --output-folder=/output

Arguments

  • --model - Model variant to use. Options:
    • jpeg-compression
    • real-image-corruptions
    • sensor-noise
    • unknown-compressions (default)
  • --no-chop - Don’t chop the image (uses more memory)

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.

Video demo

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.

Visual Results

DIV2K Validation-set (100 images)

Here are the SR results 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 results 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 results of our method on the AIM 2020 Real-Image SR test-set from the Google Drive: SRResCycGAN.