摘要
针对低光照条件下拍摄的图像受可见光低和噪声的影响,不仅降低图像在视觉上的美感而且会造成重要信息丢失的问题。本文提出结合平滑聚类和改进Retinex算法的估计照明图的低光照图像增强方法。使用平滑聚类将图像分离为细节层和基础层;利用max-RGB找到各通道最大值用于估计每个像素的照度,构建初始照明图,根据局部一致性和交替方向最小化技术优化照明图;自适应Gamma矫正对优化后的照明图进行非线性重标形成最终光照图;根据最终光照图增强输入图像,将增强后图像与细节层进行融合,获得清晰且细节更为丰富的图像;通过与LE,GC,HE,SSR,MSR,MSRCR,MSRCP算法相比,在图像HightB上,边缘强度最高达到1.00e+02,平均梯度最高达到10.5206,空间频率最高达到52.0508,图像清晰度最高达到14.6562,在主观评价和客观评价均优于其他对比算法。实验结果表明,所提算法具有良好的清晰度,更好的保留边缘和细节纹理,使用本文算法增强后的图片质量更高,细节更加丰富。
Images taken under low-light conditions are affected by low visible light and noise,which reduce the visual quality and also result in loss of important information.This article proposed a low light image enhancement method that combined smooth clustering and the improved Retinex algorithm to estimate images taken under low-light conditions.An image was separated into the detail layer and the base layer via smooth clustering.Then,max-RGB was used to find the maximum value of each channel to estimate the value of each pixel,construct the initial illumination map,and optimize this map based on local consistency and alternating direction minimization techniques.Adaptive Gamma correction performed non-linear relabeling on the optimized illumination map,providing the final illumination map.The input image could be enhanced by using the information of the final illumination map,and the enhanced image was fused with the detail layer to obtain a clearer and more detailed image.The proposed model exhibited better performance compared with the LE algorithm,GC algorithm,HE algorithm,SSR algorithm,MSR algorithm,MSRCR algorithm,and MSRCP algorithmthe edge intensity is 1.00e+02,average gradient is 10.5206,and spatial frequency is 52.0508.The highest image definition achieved is 14.6562,which is superior to other algorithms considered in this study,in both subjective and objective evaluations.The experimental results show that the proposed algorithm can generate imageswith higher definition,clearer edges,and richer textures.
作者
黄慧
董林鹭
刘小芳
赵良军
HUANG Hui;DONG Lin-lu;LIU Xiao-fang;ZHAO Liang-jun(Sichuan Key Laboratory of Artificial Intelligence, Zigong 643000, China;College of Automation and Information Engineering, Sichuan University of Science and Engineering , Zigong 64300, China;College of Computer Science and Engineering,Sichuan University of Science and Engineering, Zigong 643000, China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2020年第8期1835-1849,共15页
Optics and Precision Engineering
基金
四川省科技计划资助项目(No.2017GZ0303)
四川省院士(专家)工作站基金资助项目(No.2016YSGZZ01)
企业信息化与物联网测控技术四川省高校重点实验室开放基金资助项目(No.2019WZY04)
自贡市科技计划资助项目(N0.2019RKX03)
四川轻化工大学科研项目资助(No.2018RCL21)。
关键词
平滑聚类
低光照图像增强
Gamma矫正
光照估计
smooth clustering
low-light image enhancement
gamma correction
illumination estimation
