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Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal

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Abstract

Underwater images often exhibit severe color deviations and degraded visibility, which limits many practical applications in ocean engineering. Although extensive research has been conducted into underwater image enhancement, little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes. In this paper, we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters, which effectively removes color casts of a variety of underwater images. A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed, which circumvents the influence of white or bright regions that challenges existing physical model-based methods. To enhance contrast of resultant images, a piece-wise affine transform is applied to the transmission map estimated via background light differential. Finally, with the estimated background light and transmission map, the scene radiance is recovered by addressing an inverse problem of image formation model. Extensive experiments reveal that our results are characterized by natural appearance and genuine color, and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics, which further validates the better robustness and higher generalization ability of our enhancement model.

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Change history

  • 13 August 2022

    Incorrect cover date was used, instead of 2022 it should be 2023.

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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, Zitong Kang, Pengwei Dong, Fan Wang, Peng Ma, Jiajing Bai & Pengwei Liang

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

    Chongyi Li

Authors
  1. Bo Wang
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  2. Zitong Kang
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  3. Pengwei Dong
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  4. Fan Wang
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  5. Peng Ma
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  6. Jiajing Bai
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  7. Pengwei Liang
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  8. Chongyi Li
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Corresponding author

Correspondence to Bo Wang.

Additional information

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

Zitong Kang received the BS degree from the School of Automation and Electronic Engineering, Qingdao University of Science and Technology, China in 2019. She is currently studying in the 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.

Pengwei Dong received the BS degree in Ningxia University, China in 2020. He is currently working toward the MS degree in School of Physics and Electronic-Electrical Engineering in Ningxia University, China. His research interests include remote sensing image enhancement and computer vision.

Fan Wang received the BS degree in University of South China, China in 2020. He is currently working toward the MS degree in School of Physics and Electronic-Electrical Engineering in Ningxia University, China. His research interests include medical image segmentation and computer vision.

Peng Ma received the BS degree in Ningxia University, China in 2020. He is currently working toward the MS degree in School of Physics and Electronic Electrical Engineering in Ningxia University, China. His research interests include computer vision and machine learning.

Jiajing Bai received the BS degree in Ningxia University, China in 2020. She is currently working toward the MS degree in School of Physics and Electronic-Electrical Engineering in Ningxia University, China. Her research interests include image processing and computer vision.

Pengwei Liang received the BS degree in Tianjin University of Technology, China in 2019. He is currently working toward the MS degree in School of Physics and Electronic-Electrical Engineering in Ningxia University, China. His research interests include image processing and computer vision.

Chongyi Li received the PhD degree from the 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 the Department of Computer Science, City University of Hong Kong, China. He is currently a research fellow with the School of Computer Science and Engineering, Nanyang Technological University (NTU), 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., Kang, Z., Dong, P. et al. Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal. Front. Comput. Sci. 17, 172702 (2023). https://doi.org/10.1007/s11704-022-1205-7

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