Low-Light Image Enhancement Based on Retinex Theory

Low-light images will likely suffer from multiple issues, such as color distortion, excessive enhancement, halo artifacts, and noise amplification when their brightness is increased. The existing low-light image enhancement methods are challenging to solve the above problems simultaneously, especially with poor performance in dealing with halo artifacts and noise. In this paper, we propose a comprehensive method that can simultaneously solve multiple challenging problems by combining the characteristics of image information distribution, the advantages of various image processing technologies, and Retinex theory. Furthermore, this method can effectively suppress halo artifacts and reduce image noise. Firstly, we decompose the input image into a base layer and a detail layer. Next, we convert the base layer image to Hue Saturation Value (HSV) color space and propose an improved Retinex model to handle the Value channel separately. Moreover, we propose an image gradient denoising method based on fast guided filtering for noise reduction and detail enhancement on the detail layer image. Finally, two-layer processing results are fused in RGB space to obtain the final enhanced image. The experiment shows that our proposed method comprehensively addresses various challenging issues in existing low-light enhanced images and performs well in suppressing halo artifacts and noise.