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A novel method for single nighttime image haze removal based on gray space

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Abstract

Nighttime haze images always suffer from non-uniform illumination from artificial light sources, and most of the current dehazing algorithms are more suitable for daytime image haze removal than nighttime. In this paper, we propose a novel method for nighttime image dehazing via gray space. Firstly, we mapped the haze image from RGB color space to gray space and adopted convolutional neural network to obtain the feature distribution map of the haze. We then fused the haze feature distribution map with original image to obtain the initial haze-free image. Finally, the value and chroma of the initial haze-free image were enhanced in HSV space by improved gamma function. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms for nighttime image haze removal, especially in terms of color consistency and artifacts reduction.

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Acknowledgments

This work is supported by the National Key Research and Development Project of China(2019YFB1312102), the Natural Science Foundation of Hebei Province(F2019202364) and the Scientific Research Project of Tianjin Municipal Commission of Education (No. 2018KJ268).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China

    Yazhong Si & Fan Yang

  2. School of Information and Communication Engineering, Tianjin University Renai College, Tianjin, 301600, China

    Nannan Chong

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  1. Yazhong Si
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  2. Fan Yang
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  3. Nannan Chong
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Contributions

YS contributed to the conception of the study, experiments, and paper. FY contributed to the guidance of experiments and paper. NC participated in the experiments and paper and proposed constructive suggestions.

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Correspondence to Yazhong Si.

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Si, Y., Yang, F. & Chong, N. A novel method for single nighttime image haze removal based on gray space. Multimed Tools Appl 81, 43467–43484 (2022). https://doi.org/10.1007/s11042-022-13237-9

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  • DOI: https://doi.org/10.1007/s11042-022-13237-9

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