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

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 refl...

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Bibliographic Details
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
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11183834