Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework

Chenping Zhao, Wenlong Yue, Yingjun Wang, Jianping Wang (), Shousheng Luo, Huazhu Chen and Yan Wang
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Chenping Zhao: Postdoctoral Research Station of Physics, Henan Normal University, Xinxiang 453007, China
Wenlong Yue: School of Mathematical Science, Henan Institute of Science and Technology, Xinxiang 453003, China
Yingjun Wang: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
Jianping Wang: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Shousheng Luo: College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321000, China
Huazhu Chen: School of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou 451191, China
Yan Wang: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China

Mathematics, 2024, vol. 12, issue 24, 1-17

Abstract: Images captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancement. However, existing approaches often directly design prior regularization functions for either illumination or reflectance components, which may unintentionally introduce noise. To address these limitations, this paper presents an enhancement method by integrating a Plug-and-Play strategy into an extended decomposition model. The proposed model consists of three main components: an extended decomposition term, an iterative reweighting regularization function for the illumination component, and a Plug-and-Play refinement term applied to the reflectance component. The extended decomposition enables a more precise representation of image components, while the iterative reweighting mechanism allows for gentle smoothing near edges and brighter areas while applying more pronounced smoothing in darker regions. Additionally, the Plug-and-Play framework incorporates off-the-shelf image denoising filters to effectively suppress noise and preserve useful image details. Extensive experiments on several datasets confirm that the proposed method consistently outperforms existing techniques.

Keywords: low-light image; decomposition model; iterative reweighting regularization; Plug-and-Play (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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