Low-light Image Enhancement via a Frequency-based Model with Structure and Texture Decomposition

Mingliang ZHOU*, Hongyue LENG, Bin FANG, Tao XIANG, Xuekai WEI, Weijia JIA

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website1.

Original languageEnglish
Article number187
Number of pages23
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume19
Issue number6
Early online date31 May 2023
DOIs
Publication statusPublished - Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • denosing
  • low-light image enhancement
  • Retinex theory

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