A Structure Preservation and Denoising Low-Light Enhancement Model via Coefficient of Variation

Xingtai WU, Bin WU, Jingyuan HE, Bin FANG, Zhaowei SHANG, Mingliang ZHOU*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a structure-preserving and denoising low-light enhancement method that uses the coefficient of variation. First, we use the coefficient of variation to process the original low-light image, which is used to obtain the enhanced illumination gradient reference map. Second, we use the total variation (TV) norm to regularize the reflectance gradient, which is used to maintain the smoothness of the image and eliminate the artifacts in the reflectance estimation. Finally, we combine the above two constraint terms with the Retinex theory, which contains the denoising regular term. The final enhanced and denoised low-light image is obtained by iterative solution. Experimental results show that our method can achieve superior performance in both subjective and objective assessments compared with other state-of-the-art methods (the source code is available at: https://github.com/bbxavi/SPDLEM.).

Original languageEnglish
Article number2254018
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume36
Issue number13
Early online date22 Oct 2022
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 World Scientific Publishing Company.

Keywords

  • coefficient of variation
  • denoising
  • image enhancement
  • low-light image
  • Retinex model
  • structure-preserving

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