梯度稀疏和最小平方约束下的低照度图像分解及细节增强

低照度图像存在细节模糊、对比度低等问题.针对这些问题，本文提出一种低照度彩色图像增强算法.首先建立梯度稀疏和最小平方约束模型，将图像分解为结构层和细节层；然后采用提出的多尺度边缘保护细节增强算法强化图像的细节信息并滤波；最后把细节增强的图像经改进的Retinex算法映射，最终得到细节增强、亮度适宜、对比度较强的修复图像.实验结果表明，主观上：图像细节增强，亮度适宜；客观上：结构层图像的一维像素线性图显示其平滑特性效果较好，细节增强图的NIQE（5.5202）、BRISQE（31.1893）和PSNR（25.3625）特征较好，修复图像的熵值（7.4421）、边缘强度（128.3231）和平均亮度（121.1827）较好.本文算法实现了对低照度图像的有效分解及细节增强，并提高了图像综合质量.

Low Illumination Image Decomposition and Details Enhancement Under Gradient Sparse and Least Square Constraint

Low illumination images had the problems of fuzzy,low contrast and so on.In order to solve these problems,we put forward a low illumination image enhancement algorithm.Firstly,we established the gradient sparse and least square constraint model and decomposed the image into structure layer and detail layer.Then,the detail layer was enhanced by multi-scale edge-preserved algorithm and we used the Guided Filter to eliminate noise.Finally,the enhanced image was mapped by modified Retinex,we got the details enhanced,suitable brightness image.Experimental results show that performance is good,the 1D example figure of the contour is better than others,the figures of the details enhanced image NIQE(5.5202),BRISQE(31.1893) and PSNR(25.3625) are better,the Entroy(7.4421),Edge-Intensity(128.3231) and L-mean(121.1827) of the completed image are better as well.So the proposed algorithm shows a good performance in image enhancement.