Image Super-Resolution Reconstruction Based on Lightweight Multi-Scale Channel Attention Network

ZHOU Deng-wen; LI Wen-bin; LI Jin-xin; HUANG Zhi-yong

doi:10.12263/DZXB.20201089

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Image Super-Resolution Reconstruction Based on Lightweight Multi-Scale Channel Attention Network

PAPERS | 更新时间：2025-12-08

- Image Super-Resolution Reconstruction Based on Lightweight Multi-Scale Channel Attention Network
- ACTA ELECTRONICA SINICA Vol. 50, Issue 10, Pages: 2336-2346(2022)
- 作者机构：
  
  华北电力大学控制与计算机工程学院，北京 102206
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201089
  CLC： TP391;
- Received：30 September 2020，
  
  Revised：2021-05-14，
  
  Published：25 October 2022
- 稿件说明：
移动端阅览
周登文,李文斌,李金新等.一种轻量级的多尺度通道注意图像超分辨率重建网络[J].电子学报,2022,50(10):2336-2346.

ZHOU Deng-wen,LI Wen-bin,LI Jin-xin,et al.Image Super-Resolution Reconstruction Based on Lightweight Multi-Scale Channel Attention Network[J].ACTA ELECTRONICA SINICA,2022,50(10):2336-2346.
周登文,李文斌,李金新等.一种轻量级的多尺度通道注意图像超分辨率重建网络[J].电子学报,2022,50(10):2336-2346. DOI： 10.12263/DZXB.20201089.

ZHOU Deng-wen,LI Wen-bin,LI Jin-xin,et al.Image Super-Resolution Reconstruction Based on Lightweight Multi-Scale Channel Attention Network[J].ACTA ELECTRONICA SINICA,2022,50(10):2336-2346. DOI： 10.12263/DZXB.20201089.

摘要

近年来，基于深度卷积神经网络的图像超分辨率技术取得了突出进展，并主导了当前的超分辨率技术的研究.但是，性能的改进，往往以参数量的急剧增加为代价，这限制了超分辨率方法的实际应用.本文设计了一个轻量级单图像超分辨率深度卷积网络，主要贡献包括：提出了一个多尺度的特征融合模块，使用不同感受野的卷积核，提取多种尺度的特征；提出了一个通道搅乱注意力模块，促进特征通道之间的信息流动，并增强特征选择能力；提出了一个全局特征融合连接模块，提高浅层特征的利用率.实验证明，本文方法与当前代表性的方法MSRN（Multi-Scale Residual Network）相比，参数量减少了3/4，重建的高分辨率图像的主观和客观质量均显著更好.

Abstract

Recently

image super-resolution technology based on deep convolutional neural network has made remarkable achievements and has become popular in the current super-resolution technology. However

superior performance is often at the expense of the large number of parameter amounts

which limits the real-world applications for single image super-resolution. In this paper

a lightweight single image super-resolution deep convolutional network is proposed. The main contributions of this paper are as follows: a multi-scale feature fusion block is proposed to extract multiple features via convolution kernels with different receptive fields; the channel shuffle attention mechanism we designed promotes the flow of the information across feature channels

which enhances the ability of feature selection; a global feature fusion connection is proposed to improve the feature utilization. Extensive experiments demonstrate that the parameter amounts of our method reduced by 3/4 compared with the current state-of-the-art MSRN method

while subjective visual and objective quality of the reconstructed high-resolution image are perform significantly better.

关键词

Keywords

references

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