Awesome

Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

This repository is for CSNLN introduced in the following paper

Yiqun Mei, Yuchen Fan, Yuqian Zhou, Lichao Huang, Thomas S. Huang, and Humphrey Shi, "Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining", CVPR2020, [arXiv]

The code is built on EDSR (PyTorch) and tested on Ubuntu 18.04 environment (Python3.6, PyTorch_1.1.0) with Titan /Xp, V100 GPUs.

Contents

1. Introduction
2. Train
3. Test
4. Results
5. Citation
6. Acknowledgements

Introduction

Deep convolution-based single image super-resolution (SISR) networks embrace the benefits of learning from large-scale external image resources for local recovery, yet most existing works have ignored the long-range feature-wise similarities in natural images. Some recent works have successfully leveraged this intrinsic feature correlation by exploring non-local attention modules. However, none of the current deep models have studied another inherent property of images: cross-scale feature correlation. In this paper, we propose the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network. By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution (LR) image. The performance of SISR is significantly improved by exhaustively integrating all possible priors. Extensive experiments demonstrate the effectiveness of the proposed CS-NL module by setting new state-of-the-arts on multiple SISR benchmarks.

Cross-Scale Non-Local Attention.

The recurrent architecture with Self-Exemplars Mining (SEM) Cell.

Train

Prepare training data

1. Download DIV2K training data (800 training + 100 validtion images) from DIV2K dataset or SNU_CVLab.

2. Specify '--dir_data' based on the HR and LR images path.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

1. (optional) Download pretrained models for our paper.

   All the models and visual results can be downloaded from Google Drive

2. Cd to 'src', run the following script to train models.

   Example command is in the file 'demo.sh'.

   # Example X2 SR
   python3 main.py --chop --batch_size 16 --model CSNLN --scale 2 --patch_size 96 --save CSNLN_x2 --n_feats 128 --depth 12 --data_train DIV2K --save_models
   
   

Test

Quick start

1. Download benchmark datasets from SNU_CVLab

2. (optional) Download pretrained models for our paper.

   All the models can be downloaded from Google Drive

3. Cd to 'src', run the following scripts.

   Example command is in the file 'demo.sh'.

   # No self-ensemble: CSNLN
   # Example X2 SR
   python3 main.py --model CSNLN --data_test Set5+Set14+B100+Urban100 --data_range 801-900 --scale 2 --n_feats 128 --depth 12 --pre_train ../models/model_x2.pt --save_results --test_only --chop
   

Results

Quantitative Results

For more results, please refer to our paper and Supplementary Materials.

Visual Results

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@inproceedings{Mei2020image,
  title={Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining},
  author={Mei, Yiqun and Fan, Yuchen and Zhou, Yuqian and Huang, Lichao and Huang, Thomas S and Shi, Humphrey},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

Acknowledgements

This code is built on EDSR (PyTorch) and generative-inpainting-pytorch. We thank the authors for sharing their codes.