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

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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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Notes

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Funding

The research was supported by National Key Research and Development Program under Grant No. 2018YFB0804203, the National Natural Science Foundation of China (Grant Nos. 61772171, and 61702156), Shanghai Philosophy and Social Science Planning Project (A1713).

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Authors and Affiliations

  1. Hefei University of Technology, Hefei, 230009, Anhui Province, China

    Shijie Hao & Xu Han

  2. Northeastern University At Qinhuangdao, Qinhuangdao, 066004, Hebei Province, China

    Youming Zhang

  3. Shanghai Polytechnic University, Shanghai City, 201209, China

    Lei Xu

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  1. Shijie Hao
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  2. Xu Han
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  3. Youming Zhang
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  4. Lei Xu
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Correspondence to Lei Xu.

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Hao, S., Han, X., Zhang, Y. et al. Low-light enhancement based on an improved simplified Retinex model via fast illumination map refinement. Pattern Anal Applic 24, 321–332 (2021). https://doi.org/10.1007/s10044-020-00908-2

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  • DOI: https://doi.org/10.1007/s10044-020-00908-2

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