结合色彩校正和结构信息的双路低光照图像增强  

Dual low-light images combining color correction and structural information enhance

作　　者：林珊玲 陈燕 张雪[1,2] 林志贤 林坚普[1,2] 吕珊红 郭太良 Lin Shanling;Chen Yan;Zhang Xue;Lin Zhixian;Lin Jianpu;Lv Shanhong;Guo Tailiang(School of Advanced Manufacturing,Fuzhou University,Quanzhou,Fujian 362200,China;Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China,Fuzhou,Fujian 350116,China;School of Physics and Information Engineering,Fuzhou University,Fuzhou,Fujian 350116,China)

机构地区：[1]福州大学先进制造学院,福建泉州362200 [2]中国福建光电信息科学与技术创新实验室,福建福州350116 [3]福州大学物理与信息工程学院,福建福州350116

出　　处：《光电工程》2024年第9期75-88,共14页Opto-Electronic Engineering

基　　金：国家重点研发计划资助项目(2023YFB3609400);国家自然科学基金青年基金资助项目(62101132)。

摘　　要：为改善暗环境下图像的成像效果,本文提出了一种无监督双路低光照图像增强算法,结合了色彩校正和结构信息。该算法基于生成对抗网络,其中生成器采用双分支结构同时处理图像的色彩与结构细节,以实现更自然的颜色恢复和更清晰的纹理细节。判别器引入空间辨别模块(spatial-discriminative block,SDB),以增强其判别能力,推动生成器生成更真实的图像。图像色彩校正模块(Illumination-guided color correction block,IGCB)利用光照特征引导,减少低光照环境下噪声和伪影的影响。通过多尺度通道融合模块(selective kernel channel fusion,SKCF)和优化的注意力卷积模块(convolution attention block,CAB),增强了图像的语义信息和局部细节。实验结果表明,该算法在LOL和LSRW数据集上表现优于经典方法,在LOLv1和LOLv2数据集上,PSNR和SSIM指标分别达到19.89与0.672,以及20.08与0.693,整体性能优于现有无监督算法。实际应用验证了该算法在恢复低光照图像的亮度、对比度和色彩方面的有效性。To enhance image quality in low-light conditions,an unsupervised dual-path low-light image enhancement algorithm is proposed,integrating color correction and structural information.The algorithm utilizes a generative adversarial network(GAN)with a generator that employs a dual-branch architecture to concurrently handle color and structural details,resulting in natural color restoration and clear texture details.A spatial-discriminative block(SDB)is introduced in the discriminator to improve its judgment capability,leading to more realistic image generation.An illumination-guided color correction block(IGCB)uses illumination features to mitigate noise and artifacts in low-light environments.The selective kernel channel fusion(SKCF)and convolution attention block(CAB)modules enhance the semantic and local details of the image.Experimental results show that the algorithm outperforms classical methods on the LOL and LSRW datasets,achieving PSNR and SSIM scores of 19.89 and 0.672,respectively,on the LOLv1 dataset,and 20.08 and 0.693 on the LOLv2 dataset.Practical applications confirm its effectiveness in restoring brightness,contrast,and color in low-light images.

关 键 词：低光照图像增强 色彩校正 注意力卷积 多尺度特征融合 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]