Image Enhancement Algorithm Based on Texture Prior and Color Clustering

To address the problems of traditional multiscale retinex with color restoration (MSRCR), such as texture information weakening, partial information loss, and poor enhancement effects, an image enhancement algorithm based on texture priors and color clustering is proposed. First, prior to image enhancement, texture information is extracted for further processing. Second, considering the uneven illumination distribution, a color-clustering algorithm is proposed for image segmentation enhancement. In addition, for logarithmic domain mapping, a mapping scheme based on mean square value and mean square error is proposed based on block processing. Finally, in evaluating the enhancement algorithm, information entropy and natural statistics of the image are used to evaluate the effectiveness of the enhanced image. The experimental results show that the average entropy of the proposed method reached 7.4934 and the average of natural statistical properties reached 4.0903. The algorithm effectively enhances the details of the image, makes the image more natural, and further improves image quality.