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dSRVAE (Generative Variational AutoEncoder for Real Image Super-Resolution)

By Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani and Wan-Chi Siu

This repo only provides simple testing codes, pretrained models and the network strategy demo.

We propose a joint image denoising and Super-Resolution model by using generative Variational AutoEncoder (dSRVAE)

We participate CVPRW NTIRE2020 Real Image Super-Resolution Challenge

Please check our paper

BibTex

    @InProceedings{Liu2020dsrvae,
        author = {Zhi-Song Liu, Wan-Chi Siu and Li-Wen Wang and Chu-Tak Li and Marie-Paule Cani and Yui-Lam Chan},
        title = {Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder},
        booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition Workshop(CVPRW)},
        month = {June},
        year = {2020}
    }

For proposed dSRVAE model, we claim the following points:

• First working on using Variational AutoEncoder for image denoising.

• Then the Super-Resolution Sub-Network (SRSN) is attached as a small overhead to the DAE which forms the proposed dSRVAE to output super-resolved images.

Dependencies

Python > 3.0
OpenCV library
Pytorch > 1.0
NVIDIA GPU + CUDA
pytorch-gan-metrics

Complete Architecture

The complete architecture is shown as follows,

network

Implementation

1. Quick testing

  1. Download pre-trained models from

https://drive.google.com/open?id=1SUZGE04vw5_yDYiw6PJ4sbHAOIEV6TJ7

and copy them to the folder "models"

  1. Copy your image to folder "Test" and run
$ python test.py

The SR images will be in folder "Result" 3. For self-ensemble, run

$ python test_enhance.py
  1. GAN feature evaluation
# download statistics.npz from http://bioinf.jku.at/research/ttur/
from pytorch_gan_metrics import get_inception_score, get_fid

images = ... # [N, 3, H, W] normalized to [0, 1]
IS, IS_std = get_inception_score(images)        # Inception Score
FID = get_fid(images, 'path/to/statistics.npz') # Frechet Inception Distance

2. Testing for NTIRE 20202

s1. Testing images on NTIRE2020 Real World Super-Resolution Challenge - Track 1: Image Processing artifacts can be downloaded from the following link:

https://drive.google.com/open?id=10ZutE-0idGFW0KUyfZ5-2aVSiA-1qUCV

s2. Testing images on NTIRE2020 Real World Super-Resolution Challenge - Track 2: Smartphone Images can be downloaded from the following link:

https://drive.google.com/open?id=1_R4kRO_029g-HNAzPobo4-xwp86bMZLW

s3. Validation images on NTIRE2020 Real World Super-Resolution Challenge - Track 1 and Track 2 can be downloaded from the following link:

https://drive.google.com/open?id=1nKEJ4N2V-0NFicfJxm8AJqsjXoGMYjMp

3. Training

s1. Download the training images from NTIRE2020.

https://competitions.codalab.org/competitions/22220#learn_the_details

s2. Start training on Pytorch

  1. Train the Denoising VAE by running
$ python main_denoiser.py
  1. Train the super-resolution SRSN overhead by running
$ python main_GAN.py

Partial image visual comparison

1. Visualization comparison

Results on 4x image SR on Track 1 dataset figure2 figure3 figure4

Reference

You may check our newly work on General image super-resolution using VAE

You may also check our work on Reference based face SR using VAE

You may also check our work on Reference based General image SR using VAE

Special thanks to the contributions of Jakub M. Tomczak for their VAE with a VampPrior on KL loss calculation.