重庆工商大学学报（自然科学版）2024，Vol.41Issue(4)：86-96,11.DOI:10.16055/j.issn.1672-058X.2024.0004.011
融合残差结构与注意力机制的暗光图像增强算法
Low-light Image Enhancement Algorithm Integrating Residual Structure and Attention Mechanism
摘要
Abstract
Objective Low-light images can be obtained due to either low-light conditions or shooting techniques.In order to solve such problems as low contrast,high noise,and color distortion of images,a convolutional neural network enhancement model RetKIND was proposed,which included a decomposition network,brightness adjustment network,and noise reduction network.Methods With the help of the residual block(RB)and skip connection,the noise generated by the decomposition network during decomposition was effectively suppressed.The noise reduction network was constructed by integrating U-Net architecture,dilated convolution,and EBAM efficient attention mechanism.The dilated convolution was used to enlarge the receptive field to extract more image information and EBAM was utilized to extract details,texture,color,and other features of the reflection image in channel and space to achieve image denoising.The brightness adjustment network was composed of a UC module(self-designed module in the brightness adjustment network)and traditional convolution,which aimed to reduce the detail loss of light images and improve the contrast of the light component.Finally,the denoised reflection component and the enhanced light component were fused to obtain the normal illumination image.Results Simulation results showed that on the dataset LOL,compared with R2RNet,the values of FPSNR and FSSIM increased by 6.2％and 4.2％,respectively;compared with URetinex-Net,the values of FPSNR and FSSIM of Ret-KIND increased by 5.9％and 1.2％,respectively;compared with DEANet,the values of FPSNR and FSSIM of Ret-KIND increased by 2.9％and 1.1％,respectively.Conclusion The Ret-KIND model can not only improve image brightness,but also reduce image noise,which helps to promote the application of the low-light image enhancement model to the field of target detection.关键词
低光照图像增强/去噪/RB残差模块/EBAM注意力机制/Retinex理论Key words
low-light image enhancement/denoising/RB residual module/EBAM attention mechanism/Retinex theory分类
信息技术与安全科学引用本文复制引用
刘瑶,贾晓芬..融合残差结构与注意力机制的暗光图像增强算法[J].重庆工商大学学报（自然科学版）,2024,41(4):86-96,11.基金项目
国家自然科学基金面上项目(52174141) （52174141）
安徽省重点研究与开发计划资助项目(202104A07020005) （202104A07020005）
安徽省自然科学基金面上项目(2108085ME158) （2108085ME158）
安徽高校协同创新项目(GXXT-2020-54). （GXXT-2020-54）
