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Low-light image enhancement based on multi-illumination estimation

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Abstract

Images captured by cameras in low-light conditions have low quality and appear dark due to insufficient light exposure, which critically affects the view. Most of the traditional enhancement methods are based on the entire image for exposure enhancement, so overexposed areas in the image have the risk of secondary enhancement. In order to fully consider the exposure in low-light images, we propose a low-light image enhancement based on multi-illumination estimation, which can robustly produce high-quality results for various underexposures. The core of the proposed method is to derive multiple exposure correction images using light estimation. Then, we used a Laplacian multi-scale fusion method to combine the weight map and the images with different degrees of exposure. We used gamma correction and inversion on the original image to produce images with different exposure levels (such as underexposure, overexposure, and partial area overexposure and underexposure). The gamma-corrected image is used for lighting adjustment of underexposed areas in low-light images, while the inversion image is used for adjustment of the overexposed regions. We performed experiments on various images using multiple methods and evaluated and compared the experimental results, qualitatively and quantitatively. Experimental results show that the proposed method in this study can effectively eliminate the effects of low light and improve image quality.

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Acknowledgments

The authors acknowledge the National Natural Science Foundation of China (Grant nos. 61772319, 61976125, 61873177 and 61773244), and Shandong Natural Science Foundation of China (Grant no. ZR2017MF049).

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Authors and Affiliations

  1. Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Yantai, 264005, China

    Xiaomei Feng, Jinjiang Li, Zhen Hua & Fan Zhang

  2. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China

    Xiaomei Feng & Zhen Hua

  3. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China

    Jinjiang Li & Fan Zhang

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  1. Xiaomei Feng
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  2. Jinjiang Li
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Correspondence to Jinjiang Li.

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Feng, X., Li, J., Hua, Z. et al. Low-light image enhancement based on multi-illumination estimation. Appl Intell 51, 5111–5131 (2021). https://doi.org/10.1007/s10489-020-02119-y

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