Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

Abstract

Improving low-light images is used in many medical, military, and other applications, such as classification, detection, and increasing the clarity of targets. In this study, we improved low-light images by relying on three main techniques. The first is (DCP), the second is color retrieval, and the third is improving the lighting component based on the Lab color space, where the color components are isolated from the light, and then mapping is done using intensity stretch and (adapted histogram equalization). LOL data (containing 485 low-light images) were enhanced, with two types of standards being reference peak signal to noise ratio (PSNR) and non-reference scalas as entropy (EN), average gradient (AG), and natural image quality evaluator (NIQE). By analyzing the results, the proposed method obtained an improvement, and the quality standards reached EN (7.550), AG (9.978), NIQE (3.499), and PSNR (15.013) values. Compared to a number of recent algorithms, which indicates its success in increasing the lighting and contrast in these images.