Low-Light Image Enhancement With Semi-Decoupled Decomposition

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Low-Light Image Enhancement With Semi-Decoupled Decomposition

Shijie Hao; Xu Han; Yanrong Guo; Xin Xu; Meng Wang

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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