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

Chenping Zhao (), Wenlong Yue, Jianlou Xu and Huazhu Chen
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Chenping Zhao: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Wenlong Yue: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Jianlou Xu: School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
Huazhu Chen: School of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou 451191, China

Mathematics, 2023, vol. 11, issue 18, 1-14

Abstract: 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.

Keywords: low light; enhancement and denoising; Retinex; decomposition model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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