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LOW-LIGHT IMAGE ENHANCEMENT VIA WEIGHTED FRACTIONAL-ORDER MODEL.

Authors :
Jun LI
Chao YAN
Qinglu HOU
Weiwei ZHOU
YIN GAO
Source :
Computing & Informatics; 2024, Vol. 43 Issue 2, p343-368, 26p
Publication Year :
2024

Abstract

Low-light image enhancement (LLIE) enables to serve high-level vision tasks and improve their efficiency. Retinex-based methods have well been recognized as a representative technique for LLIE, but they still suffer from inflexible regularization terms in decomposing illumination and reflectance. In this paper, we propose a new weighted fractional-order variational model based on the Retinex model. First, the constructed weighted fractional-order variational model estimates piecewise smoothed and weakly pixel-shifted illumination by aware structures and textures. Then, to solve this problem accurately, we chose a semi-decoupled approach and an alternating minimization method. Finally, the designed multi-illumination fusion method accurately enhances the structure-rich dark regions of the image through well-exposedness and local entropy weights, while realizing adaptive enhancement based on a naturalness-preserving parameter estimation algorithm. The results of subjective and objective experiments on several challenging low-light datasets demonstrate that our proposed method shows better competitiveness in enhancing low-light images compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13359150
Volume :
43
Issue :
2
Database :
Supplemental Index
Journal :
Computing & Informatics
Publication Type :
Academic Journal
Accession number :
178201528
Full Text :
https://doi.org/10.31577/cai_2024_2_343