Multiscale Low-Light Image Enhancement Algorithm with Brightness Equalization and Edge Enhancement Algorithm

To address issues such as detail loss, artifacts, and unnatural appearance associated with current low-illumination image enhancement algorithms, a multiscale low-illumination image enhancement algorithm based on brightness equalization and edge enhancement is proposed in this study. Initially, an improved Sobel operator is employed to extract edge details, yielding an image with enhanced edge details. Subsequently, the brightness component (V) of the HSV color space is enhanced using Retinex, and brightness equalization is accomplished via improved Gamma correction, yielding an image with balanced brightness. The Laplacian weight graph, significance weight graph, and saturation weight graph are computed for the edge detail-enhanced image and brightness-balanced image, culminating in the generation of a normalized weight graph. This graph is then decomposed into a Gaussian pyramid, while the edge detail-enhanced image and brightness-balanced image are decomposed into a Laplacian pyramid. Finally, a multiscale pyramid fusion strategy is employed to merge the images, resulting in the final enhanced image. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms on the LOL dataset in terms of average peak signal to noise ratio, structural similarity, and naturalness image quality evaluator. This algorithm effectively enhances the contrast and clarity of low-illumination images, resulting in images with richer detail information, improved color saturation, and considerably enhanced quality.