基于光照图估计的Retinex低照度图像增强算法

Retinex Low-Illumination Image Enhancement Algorithm Based on Light Image Estimation

针对低照度环境下采集的图像存在对比度较低、细节丢失、噪声干扰等问题,提出一种基于Retinex的光照图估计改进算法,以实现低照度图像增强。计算R、G、B 3个颜色通道中的最大值,并用L_(2)范数对光照进行近似,运用基于相对总变差形式的改进模型对亮通道进行平滑细化及自适应Gamma校正,并利用Retinex模型进行图像增强。在MATLAB仿真平台上对不同的低照度图像进行增强处理,实验结果表明,与Retinex-Net、SRIE等典型算法相比,该算法能有效提高图像对比度与清晰度,增强图像细节信息,使图像颜色更加鲜艳自然,视觉质量更好。

The images collected in low-illumination environment are limited in the contrast,and suffer from detail loss and noise interference.To address the problem,a Retinex-based method is proposed to improve illumination map estimation and realize low-illumination image enhancement.The maximum values in the three color channels of R,G and B are calculated,and the illumination is approximated with L_(2) norm.Then an improved model based on the relative total variation is used to smooth and refine the bright channel,and implement adaptive Gamma correction.Finally,the image is enhanced by using the Retinex model.The MATLAB simulation platform is used for experiments of low-illumination image enhancement.The results show that compared with Retinex-Net,SRIE and other typical algorithms,the proposed algorithm can effectively improve image contrast and clarity,enhance image details,and make image colors more vivid and natural to improve visual effects.