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Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

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Abstract

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime, which however, raises new challenges such as severe color distortion, more complex lighting conditions, and lower contrast. Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime, we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm. We first propose a human visual system (HVS) inspired color correction model, which is effective for removing the color deviation on nighttime hazy images. Then, we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion, where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids. Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast, color fidelity, and visibility. In addition, our method outperforms the state-of-the-art methods qualitatively and quantitatively.

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Acknowledgements

This work was supported by Higher Education Scientific Research Project of Ningxia (NGY2017009).

Author information

Authors and Affiliations

  1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan, 750021, China

    Bo Wang, Li Hu, Bowen Wei & Zitong Kang

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Chongyi Li

Authors
  1. Bo Wang
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  2. Li Hu
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  3. Bowen Wei
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  4. Zitong Kang
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  5. Chongyi Li
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Corresponding author

Correspondence to Bo Wang.

Additional information

Bo Wang received his PhD degree from School of Electrical and Information Engineering, Tianjin University, China in 2016. He is currently a lecturer in School of Physics and Electronic-Electrical Engineering, Ningxia University, China. His research interests include image restoration and enhancement, image classification and medical image processing.

Li Hu received her BS degree in Ningxia University, China in 2018. She is currently working toward MS degree in School of Physics and Electronic-Electrical Engineering in Ningxia University, China. Her research interests include image processing and computer vision.

Bowen Wei received his BS degree in Ningxia Normal University, China in 2018. He is currently working toward MS degree in School of Physics and Electronic Electrical Engineering in Ningxia University, China. His research interests include computer vision and machine learning.

Zitong Kang received her BS degree from School of Automation and Electronic Engineering, Qingdao University of Science and Technology, China in 2019. She is currently studying in School of Physics, Electronic and Electrical Engineering, Ningxia University, China, majoring in Electronic and Communication Engineering. Her research interests include computer vision and data mining.

Chongyi Li received his PhD degree from School of Electrical and Information Engineering, Tianjin University, China in June 2018. From 2016 to 2017, he was a joint-training PhD Student with Australian National University, Australia. He was a postdoctoral fellow with Department of Computer Science, City University of Hong Kong, China. He is currently a research fellow with School of Computer Science and Engineering, Nanyang Technological University, Singapore. His current research focuses on image processing, computer vision, and deep learning, particularly in the domains of image restoration and enhancement.

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Wang, B., Hu, L., Wei, B. et al. Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy. Front. Comput. Sci. 16, 164706 (2022). https://doi.org/10.1007/s11704-021-0162-x

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