The work aims to solve the problem of poor imaging quality and unclear local details due to the large brightness of the image. The histogram equalization (HE) was introduced into the image information entropy domain to propose the contrast-reduced adaptive entropy histogram equalization (CRAEHE). Based on the statistical information entropy of each gray level, the original image was first divided into several sub-regions, and the statistical gray-scale information entropy of each sub-region was intercepted by thresholds and supplemented onto each gray level in the sub-region. Then, the sub-region performed the information entropy histogram equalization processing. With USC-SIPI and CBSD432 dataset images, the quality of the experimental samples was evaluated by such parameters as image gray average, standard deviation, average gradient and information entropy. The gray average of the original image processed by the proposed algorithm was decreased by 7.94%, the standard deviation was increased by 52.22% averagely, the information entropy was increased by 19.86%, and the average gradient was increased by 57.19%. The proposed algorithm enhances the details of over-lighted images selected from the datasets, and improves the overall image details and quality. The processing results of this algorithm are significantly improved compared to the subjective quality of other processed experimental samples, and the range of adaptation to light intensity is wide.