基于主成分分析的自适应低光照图像增强算法

Adaptive Low-light Image Enhancement Algorithm Based on Principal Component Analysis

针对低光照图像能见度低、颜色退化、噪声大等问题,提出了一种基于主成分分析的自适应低光照图像增强算法。将原始RGB图像转换为HSV色彩空间,提取V分量;再根据估计的光照分量调整自适应亮度增强函数的参数,并利用图像融合增强图像的V分量;将图像从HSV空间转换回RGB空间。实验结果表明,提出的算法能够保留低光照图像的细节,且能很好地平衡图像颜色。

Aiming at the problems of low visibility,color degradation and noise of low-light images,etc.an adaptive low-light image enhancement algorithm based on principal component analysis is proposed.Firstly,the original RGB image is converted to HSV color space and the V component is extracted.Then the parameters of the adaptive brightness enhancement function is adjusted according to the estimated illumination component,and the V component of the image is enhanced by image fusion.Finally,the image is converted from HSV space back to RGB space.Experimental results show that the proposed algorithm retains the details of low-light images and balances image colors well.