基于盲点网络的低照度图像自监督增强  

Self-Supervised Enhancement of Low-Light Images Based on Blind Spot Networks

作　　者：陈勇[1] 张金亮 刘焕淋[2] 邵凯鑫 陈尚明 熊杭英 张佑瑞 Chen Yong;Zhang Jinliang;Liu Huanlin;Shao Kaixin;Chen Shangming;Xiong Hangying;Zhang Yourui(Key Laboratory of Industrial Intermet of Things and Network Control,Ministry of Education,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学工业物联网与网络化教育部重点实验室,重庆400065 [2]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《光学学报》2024年第13期95-104,共10页Acta Optica Sinica

基　　金：国家自然科学基金(51977021)。

摘　　要：针对现有的低照度图像增强算法在增强的同时其图像中仍含有残留噪声、网络训练中将产生恒等映射以及成对数据集获取困难的问题,本文提出了一种基于盲点网络的自监督微光增强网络。首先,采用双边多尺度融合直方图均衡化的方法对图像亮度进行调整,以此来克服传统直方图增强方法中的信息颜色损失;其次,所设计的去噪网络可以自适应地从原始图像中进行学习,同时采用像素混洗下采样解耦相邻像素空间中的相关性;最后,为保持图像空间和颜色的一致性设计了相关的损失函数。实验证明,本文算法克服了现有算法存在残留噪声、网络训练中产生恒等映射的问题,有效地提高了低照度图像质量。Objective Images can be conceptualized as a vivid linguistic schema that communicates information and elicits emotions via distinct elements comprising lines, colors, shapes, textures, etc. The human visual apparatus demonstrates an elevated sensitivity and recognition proficiency towards these visual components, thereby amassing a lot of information and enriching experience from simple image observations. Additionally, images exert a perceptible influence on human vision.For instance, variations in color, contrast, brightness, and other factors can trigger diversified reactions within the human visual system. However, due to suboptimal environmental lighting, equipment limitations, and the photographer proficiency, the resultant images frequently fail to meet the anticipated outcomes. Among the multitude of factors impinging on image quality, the pervasive influence of environmental lighting conditions, particularly in low-light environments, is the most remarkable. Low-light images can be characterized as images captured in lighting conditions that are insufficient to fully stimulate the brightness capture function of the camera. Consequently, the output image is not even on the fringe of possessing an exemplary histogram distribution. In such predicaments, the implementation of a specialized algorithm becomes imperative to facilitate image enhancement, thereby delivering an optimized image and bolstering overall performance.Methods To solve the problems of residual noise, identity mapping in network training, and pairwise data acquisition,we propose a self-supervised low-light enhancement network based on a blind spot network. Firstly, the technique of bilateral multi-scale fusion histogram equalization is utilized to adjust the image brightness and thus overcome the information color loss prevalent in traditional histogram enhancement methods. Secondly, the designed denoising network can adaptively learn from the original image, while pixel shuffle downsampling is employed to decouple the correlation in adjac

关 键 词：图像处理 微光图像增强 图像去噪 盲点网络 直方图均衡化 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]