Low-Light Image Enhancement With Semi-Decoupled Decomposition

Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a rep...

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Vydáno v:IEEE transactions on multimedia Ročník 22; číslo 12; s. 3025 - 3038
Hlavní autoři: Hao, Shijie, Han, Xu, Guo, Yanrong, Xu, Xin, Wang, Meng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Decomposition
Image edge detection
Image enhancement
Image quality
Imaging
Lighting
Low-light images
Object recognition
Optimization
Regularization
Retinex model
Visibility
Visualization
ISSN:1520-9210, 1941-0077
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Shrnutí:Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a representative technique for this task, but they still have the following limitations. First, due to less-effective image decomposition or strong imaging noise, various artifacts can still be brought into enhanced results. Second, although the priori information can be explored to partially solve the first issue, it requires to carefully model the priori by a regularization term and usually makes the optimization process complicated. In this paper, we address these issues by proposing a novel Retinex-based low-light image enhancement method, in which the Retinex image decomposition is achieved in an efficient semi-decoupled way. Specifically, the illumination layer I is gradually estimated only with the input image S based on the proposed Gaussian Total Variation model, while the reflectance layer R is jointly estimated by S and the intermediate I. In addition, the imaging noise can be simultaneously suppressed during the estimation of R. Experimental results on several public datasets demonstrate that our method produces images with both higher visibility and better visual quality, which outperforms the state-of-the-art low-light enhancement methods in terms of several objective and subjective evaluation metrics.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2020.2969790