Low-light image enhancement based on variational Retinex model

Xuesong Li; Hongyi Zhang; Jinfeng Pan; Qilei Li; Guofeng Zou; Mingliang Gao

doi:10.1117/12.2631428

1 May 2022 Low-light image enhancement based on variational Retinex model

Xuesong Li, Hongyi Zhang, Jinfeng Pan, Qilei Li, Guofeng Zou, Mingliang Gao

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 1217108 (2022) https://doi.org/10.1117/12.2631428
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

The low-light image enhancement plays a crucial role in computer vision and multimedia applications. However, it is still a challenging task, as the degraded images reduce the visual naturalness and visibility. To address this problem, we build a novel variational Retinex model to accurately estimate the illumination and reflectance components. The illumination and reflectance are jointly updated by alternating optimization algorithm. Experimental results on several public datasets demonstrate that the proposed method outperforms the state-of-the-art methods in Retinex decomposition and illumination adjustment.

Citation Download Citation

Xuesong Li, Hongyi Zhang, Jinfeng Pan, Qilei Li, Guofeng Zou, and Mingliang Gao "Low-light image enhancement based on variational Retinex model", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 1217108 (1 May 2022); https://doi.org/10.1117/12.2631428

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KEYWORDS

Image enhancement

Reflectivity

Image quality

RGB color model

Optimization (mathematics)

Visualization

Volume rendering

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