基于自适应特性二维经验模式分解的Retinex彩色图像增强

摘要

提出一种彩色图像自适应增强方法:将图像从RGB色彩空间转化到HSV色彩空间并保持H分量不变,对亮度分量V通过自适应特性二维经验模式分解(ABEMD)估算其照度分量,再根据中心/环绕Retinex算法计算出反射分量,对照度和反射分量分别应用Gamma校正和Weber定律,并进行加权运算,基于全局特性自适应地调整S分量,并将图像从HSV色彩空间转化回RGB色彩空间.最后利用主观和客观的方法对实验结果进行了评价,实验表明了该算法在均值、方差、信息熵和清晰度方面均优于MSR算法和Meylan的算法.%In this paper, an adaptive color image enhancement method was proposed: Firstly, color image was transformed from RGB to HSV color space and the H component was kept invariable, while the illumination component of brightness image could be estimated through Adaptive Bidimensional Empirical Mode Decomposition (ABEMD); Secondly,reflection component was figured out by the method of center/surround Retinex algorithm, and the illumination and reflection components were controlled through Gamma emendation and Weber's law and processed with weighted average method;Thirdly, the S component was adjusted adaptively based on characteristics of the whole image, and then image was transformed back to RGB color space. The method could be evaluated by subjective effects and objective image quality assessment, and the experiment results show that the proposed algorithm is better in mean value, square variation, entropy and resolution than MSR algorithm and Meylan's algorithm.