HAN

PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

This repository is for HAN introduced in the following paper

Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, Haifeng Shen, "Single Image Super-Resolution via a Holistic Attention Network", ECCV 2020, arxiv

The code is built on RCAN (PyTorch) and tested on Ubuntu 16.04/18.04 environment (Python3.6, PyTorch_0.4.0, CUDA8.0, cuDNN5.1) with Titan X/1080Ti/Xp GPUs.

Contents

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1. Introduction
2. Train
3. Test
4. Acknowledgements

Introduction

Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super- resolution approaches.

Train Prepare training data Download DIV2K training data (800 training + 100 validtion images) from DIV2K dataset.

Begin to train

(optional) Download models for our paper and place them in '/HAN/experiment/HAN'. All the models (BIX2/3/4/8, BDX3) can be downloaded from GoogleDrive. You can use scripts in file 'demo.sh' to train models for our paper.

BI, scale 2, 3, 4, 8
#HAN BI model (x2)

python main.py --template HAN --save HANx2 --scale 2 --reset --save_results --patch_size 96 --pre_train ../experiment/model/RCAN_BIX2.pt

#HAN BI model (x3)

python main.py --template HAN --save HANx3 --scale 3 --reset --save_results --patch_size 144 --pre_train ../experiment/model/RCAN_BIX2.pt

#HAN BI model (x4)

python main.py --template HAN --save HANx4 --scale 4 --reset --save_results --patch_size 192 --pre_train ../experiment/model/RCAN_BIX2.pt

#HAN BI model (x8)

python main.py --template HAN --save HANx8 --scale 8 --reset --save_results --patch_size 384 --pre_train ../experiment/model/RCAN_BIX2.pt

Begin to Test

Quick start

Download models for our paper and place them in '/experiment/HAN'.

Cd to '/HAN/src', run the following scripts.
#test
python main.py --template HAN --data_test Set5+Set14+B100+Urban100+Manga109 --data_range 801-900 --scale 2 --pre_train ../experiment/HAN/HAN_BIX2.pt --test_only --save HANx2_test --save_results

All the models (BIX2/3/4/8, BDX3) can be downloaded from GoogleDrive.

The whole test pipeline

1.Prepare test data.

Place the original test sets in '/dataset/x4/test'.

Run 'Prepare_TestData_HR_LR.m' in Matlab to generate HR/LR images with different degradation models.

2.Conduct image SR.

See Quick start

3.Evaluate the results.

Run 'Evaluate_PSNR_SSIM.m' to obtain PSNR/SSIM values for paper.

Acknowledgements

This code is built on RCAN. We thank the authors for sharing their codes of RCAN PyTorch version.