Gradient Variance Loss

[ICASSP 2022] Official implementation of the Gradient Variance loss presented in the paper paper "Gradient Variance Loss for Structure-Enhanced Image Super-Resolution".

Requirements

for installing required packages run pip install -r requirements.txt

Usage

To train the VDSR model with the gradient variance loss run the following command

python train.py --dataroot [path to DIV2K dataset] --cuda

Introduction

"Gradient Variance Loss for Structure-Enhanced Image Super-Resolution"

By Lusine Abrahamyan, Anh Minh Truong, Wilfried Philips and Nikos Deligiannis.

Approach

We observe that gradient maps of images generated by the models trained with the L1/L2 losses have significantly lower variance than the gradient maps of the original high-resolution images.

In this work, we introduce a structure-enhancing loss function, coined Gradient Variance (GV) loss, to minimize the difference between the variances of predicted and original gradient maps and generate textures with perceptual-pleasant details.

Performance

Public benchmark test results and DIV2K validation results (PSNR(dB) / SSIM).

Citation

If you find the code useful for your research, please consider citing our works

@article{abrahamyangvloss,
  title={Gradient Variance Loss for Structure-Enhanced Image Super-Resolution},
  author={Lusine, Abrahamyan and  Anh Minh, Truong and  Wilfried, Philips and Nikos, Deligiannis},
  journal={Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  publisher = {IEEE},
  year={2022}
}

Acknowledgement

Codes for the VDSR model are from pytorch-vdsr.