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HSV空间的RetinexNet低照度图像增强算法

RetinexNet Low Illumination Image Enhancement Algorithm in HSV Space

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摘要

针对RetinexNet低照度图像增强算法中出现的颜色失真、边缘模糊等问题,提出了一种改进的RetinexNet算法。首先,利用HSV(Hue,Saturation,Value)颜色空间模型中各通道相对独立的特性,增强亮度分量;然后,利用相关系数使饱和度分量随亮度分量的变化自适应调整,避免图像色感发生变化;最后,针对增强图像的边缘模糊问题,采用Laplace算法对反射率图像进行锐化处理,增强图像的细节表达能力。实验结果表明,本算法可以有效增强图像的细节,保持图像的整体色彩和原始图像一致,提高图像的视觉效果。

Abstract

Aiming at the problem of color distortion and edge blur in RetinexNet low illumination image enhancement algorithm, we propose an improved RetinexNet algorithm. First, using the relatively independent characteristics of each channel in the HSV (Hue, Saturation, Value) color space model to enhance the brightness component. Then, the correlation coefficient is used to adaptively adjust the saturation component with the change of the brightness component to avoid changes in image color perception. Finally, aiming at the edge blur problem of the enhanced image, Laplace algorithm is adopted to sharpen the reflectivity image to enhance the ability of detail expression of the image. Experimental results show that the proposed algorithm could effectively enhance the details of the image, keep the overall color of the image consistent with the original image, and improve the visual effect of the image.

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中图分类号:TP391

DOI:10.3788/LOP57.201504

所属栏目:机器视觉

基金项目:国家重点研发计划、国家自然科学基金联合基金、中央高校基本科研业务费专项;

收稿日期:2019-12-10

修改稿日期:2020-02-25

网络出版日期:2020-10-01

作者单位    点击查看

张红颖:中国民航大学电子信息与自动化学院, 天津 300300
赵晋东:中国民航大学电子信息与自动化学院, 天津 300300

联系人作者:张红颖(carole_zhang0716@163.com)

备注:国家重点研发计划、国家自然科学基金联合基金、中央高校基本科研业务费专项;

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引用该论文

Zhang Hongying,Zhao Jindong. RetinexNet Low Illumination Image Enhancement Algorithm in HSV Space[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201504

张红颖,赵晋东. HSV空间的RetinexNet低照度图像增强算法[J]. 激光与光电子学进展, 2020, 57(20): 201504

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