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Pattern Analysis and Applications

Erschienen in:

19.09.2020 | Short Paper

Low-light enhancement based on an improved simplified Retinex model via fast illumination map refinement

verfasst von: Shijie Hao, Xu Han, Youming Zhang, Lei Xu

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2021

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Abstract

Low-light enhancement is an important post-image-processing technique, as it helps to reveal hidden details from dark image regions. In this paper, we propose a fast low-light enhancement model, which is robust to various lighting conditions and imaging noise, and is computationally efficient. By using a fusion-based simplified Retinex model, our model caters to different lighting conditions. In the model, we propose an edge-preserving filter to efficiently refine the estimated illumination map. We also extend our model by equipping it with a very simple denoising step, which effectively prevents the over-boosting of imaging noise in the dark regions. We conduct the experiments on public available images as well as the ones collected by ourselves. Visual and quantitative results validate the effectiveness of our model.

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Titel: Low-light enhancement based on an improved simplified Retinex model via fast illumination map refinement
verfasst von: Shijie Hao
Xu Han
Youming Zhang
Lei Xu
Publikationsdatum: 19.09.2020
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2021
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-020-00908-2

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