RCAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Image Super-Resolution Using Very Deep Residual Channel Attention Networks.

Table of contents

• RCAN-PyTorch
  • Overview
  • Table of contents
  • About Image Super-Resolution Using Very Deep Residual Channel Attention Networks
  • Download weights
  • Download datasets
  • Test
  • Train
  • Result
  • Credit
    • Image Super-Resolution Using Very Deep Residual Channel Attention Networks

About Image Super-Resolution Using Very Deep Residual Channel Attention Networks

If you're new to RCAN, here's an abstract straight from the paper:

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The lowresolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

Download weights

• Google Driver
• Baidu Driver

Download datasets

Contains DIV2K, DIV8K, Flickr2K, OST, T91, Set5, Set14, BSDS100 and BSDS200, etc.

• Google Driver
• Baidu Driver

Test

Modify the contents of the file as follows.

• line 29: upscale_factor change to the magnification you need to enlarge.
• line 31: mode change Set to valid mode.
• line 70: model_path change weight address after training.

Train

Modify the contents of the file as follows.

• line 29: upscale_factor change to the magnification you need to enlarge.
• line 31: mode change Set to train mode.

If you want to load weights that you've trained before, modify the contents of the file as follows.

Resume model

• line 47: start_epoch change number of model training iterations in the previous round.
• line 48: resume change to SRResNet model address that needs to be loaded.

Result

Source of original paper results: https://arxiv.org/pdf/1807.02758.pdf

In the following table, the value in () indicates the result of the project, and - indicates no test.

Dataset	Scale	PSNR
Set5	2	38.27(38.09)
Set5	3	34.74(34.56)
Set5	4	32.63(32.41)
Set5	8	27.31(26.97)

Low Resolution / Super Resolution / High Resolution

Credit

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu

Abstract
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

[Code] [Paper]

@article{DBLP:journals/corr/abs-1807-02758,
  author    = {Yulun Zhang and
               Kunpeng Li and
               Kai Li and
               Lichen Wang and
               Bineng Zhong and
               Yun Fu},
  title     = {Image Super-Resolution Using Very Deep Residual Channel Attention
               Networks},
  journal   = {CoRR},
  volume    = {abs/1807.02758},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.02758},
  eprinttype = {arXiv},
  eprint    = {1807.02758},
  timestamp = {Tue, 20 Nov 2018 12:24:39 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1807-02758.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}