An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet

Abstract

Due to the influence of the environment and the limit of optical equipment, low-light images produce problems such as low brightness, high noise, low contrast, and color distortion, which have a great impact on their visual perception and the following image understanding tasks. In this paper, we take advantage of the independent nature of YCbCr color channels and incorporate RetinexNet into the brightness channel (Y) to reduce color distortion in the enhanced images. Meanwhile, to suppress the image noise generated during the enhancement, the enhanced image is also denoised. Finally, the original color and the enhanced brightness are recombined in the channel direction, converted back to the RGB color space, and adjusted to generate an enhanced result. The proposed algorithm is compared with other recently published counterparts on the LOL dataset. The experimental results demonstrate that the proposed algorithm achieved better performance in terms of both quantitative metrics and visual quality.