Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model

• Published in: Mathematics (Basel) 2023-09, Vol.11 (18), p.3834
• Main Authors: Zhao, Chenping, Yue, Wenlong, Xu, Jianlou, Chen, Huazhu
• Format: Article
• Language: English
• Subjects: Algorithms; Decomposition; decomposition model; Deep learning; enhancement and denoising; Illumination; Image enhancement; Light; low light; Low visibility; Mathematics; Noise; Noise reduction; Reflectance; Regularization; Retinex; Retinex (algorithm); Smoothing; Teaching methods
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11183834

It is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or reflectance. Nevertheless, noise can be involved in such schemes. To address these issues, a new Retinex-based decomposition model for simultaneous enhancement and denoising has been developed. In this paper, an extended decomposition scheme is introduced to extract the illumination and reflectance components, which helps to better describe the prior information on illumination and reflectance. Subsequently, spatially adaptive weights are designed for two regularization terms. The main motivation is to provide a small amount of smoothing in near edges or bright areas and stronger smoothing in dark areas, which could preserve useful information and remove noise effectively during image-enhancement processing. Finally, the proposed algorithm is validated on several common datasets: LIME, LOL, and NPE. Extensive experiments show that the presented method is superior to state-of-the-art methods both in objective index comparisons and visual quality.