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.

Keywords

MSC

68-U10

Cite this article

@article{cai-6783,
  author  = {Jun Li and Chao Yan and Qinglu Hou and Weiwei Zhou and Yin Gao},
  title   = {Low-Light Image Enhancement via Weighted Fractional-Order Model},
  journal = {Computing and Informatics},
  volume  = {43},
  number  = {2},
  pages   = {343--368},
  year    = {2024},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2024_2_343}
}
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