An adaptive enhancement algorithm for low-illumination images based on HSV chromaticity space

Aiming at the problems of overexposure and poor local contrast in traditional low-light image enhancement algorithms, an adaptive low-light image enhancement algorithm based on HSV chromaticity space is proposed, which firstly converts the input image into RGB to HSV chromaticity space, then extracts components from the converted HSV chromaticity space, and performs convolution operation on the details respectively, and then weight fusion and conversion to RGB image by the convolution operation. Secondly, the weights of the convolved components are fused and converted to RGB images, and finally, the enhanced images are obtained by histogram equalization and contrast-brightness enhancement. The experiments demonstrate that the average PSNR value of this algorithm in 11 different scenes is improved by 44.8%, 31.35%, and 17.86%. In contrast, the SSIM value is reduced by 59.63%, 56.58%, and 25.4%, respectively, compared with the contrast-brightness image enhancement algorithm, the adaptive gamma transform algorithm, and the CLAHE algorithm.