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Low-light image enhancement algorithm based on an atmospheric physical model

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Abstract

Under low illumination, the colour constancy of human vision can be used for correctly determining the colour of objects according to the fixed reflection coefficient of external light and objects. However, video image acquisition equipment does not implement the colour constancy characteristic of the human visual system. Under low illumination, only a small amount of light is reflected from the surface of the imaged object; as a result, the captured image is underexposed. After statistical analysis of low-light images, these inverted underexposed images appear foggy. Inversion is a uniform and reversible operation that is performed on the entire image. Hereby, a method is proposed for resolving low-light images using conventional physical models. First, a low-light image is inverted for obtaining a foggy image. Subsequently, a pyramid-type dense residual block network and a dark channel prior K-means classification method are applied to the foggy image, to calculate the transmission and atmospheric light. Finally, the parameters obtained from this solution are incorporated into the low-light imaging model to obtain a clear image. We subjectively and qualitatively analysed the experimental results, and used information entropy and average gradient for objective quantitative analysis. We demonstrate that the algorithm improves the overall brightness and contrast of the imaged scenes, and the obtained enhanced images are clear and natural.

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Acknowledgment

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

    Xiaomei Feng, Jinjiang Li & Zhen Hua

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

    Xiaomei Feng & Jinjiang Li

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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. Low-light image enhancement algorithm based on an atmospheric physical model. Multimed Tools Appl 79, 32973–32997 (2020). https://doi.org/10.1007/s11042-020-09562-6

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