计算机与现代化Issue(3)：85-91,7.DOI:10.3969/j.issn.1006-2475.2024.03.014
基于双重注意力残差模块的低照度图像增强
Low-light Image Enhancement Based on Dual Attention Residual Blocks
摘要
Abstract
Low Light Image Enhancement(LLIE),which is to restore images captured under insufficient lighting conditions to normal exposure images.The existing LLIE algorithms based on deep learning often use stacked convolution or up/down sampling methods,which lacks the guidance of relevant semantic information,resulting in problems such as increased noise,color distor-tion and detail loss in the enhanced image.To address this issue,a novel LLIE algorithm based on dual attention residual mod-ules is proposed.This algorithm proposes a residual block that integrates dual attention units(Dual Attention Residual Block,DA-ResBlock),which provides semantic information guidance in both channel and spatial domains.Through multi-level cas-caded DA-ResBlocks,effective features are stably extracted,and skip connections and convolutional neural networks are used to restore image detail information.In addition,a composite loss function is used to constrain the enhancement task.Finally,we compare our algorithm with mainstream algorithms in recent years on two public datasets that provide real images.The experimen-tal results show that the proposed algorithm effectively improves image brightness while better suppressing noise,restoring image color and detail texture in subjective vision.In the objective evaluation,the three indexes of PSNR,SSIM and LPIPS are supe-rior to the compared mainstream algorithms.关键词
图像增强/低照度图像/视觉注意力/残差网络Key words
image enhancement/low-light image/visual attention/residual network分类
信息技术与安全科学引用本文复制引用
杜韩宇,魏延,唐保香,廖恒锋,叶思佳..基于双重注意力残差模块的低照度图像增强[J].计算机与现代化,2024,(3):85-91,7.基金项目
重庆市技术创新与应用发展重点项目(cstc2019jscx-mbdxX0061) （cstc2019jscx-mbdxX0061）
