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Underwater image dehazing and denoising via curvature variation regularization

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Abstract

Challenges for underwater captured image processing often lie in images degraded with haze, noise and low contrast, caused by absorption and scattering of the light during propagation. In this paper, we aim to establish a novel total variation and curvature based approach that can properly deal with these problems to achieve dehazing and denoising simultaneously. Integration with the underwater image formation model is successfully realized by formulating the global background light and the transmission map derived from the improved dark channel prior and underwater red channel prior into our variational framework respectively. Moreover, the generated non-smooth optimization problem is solved by the alternating direction method of multipliers (ADMM). Extensive experiments including real underwater image application tests and convergence curves display the significant gains of the proposed variational curvature model and developed ADMM  algorithm. Qualitative and quantitative comparisons with several state-of-the-art methods as well as four evaluation metrics are further conducted to quantify the improvements of our fusion approach.

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Acknowledgements

The research work is partially supported by National Natural Science Foundation of China (No. 61901240), China Scholarship Council (No. 201908370002), the Natural Science Foundation of Shandong Province, China (No. ZR2019BF042), and the China Postdoctoral Science Foundation (No. 2017M612204). The first author would like to thank Lu Tan for doing many researches about variational method based on image analysis, also thank Xiangjun Du, Yi Zhao, and Xiaopeng Wang, who work in the experimental teaching centre for providing us with the experimental platform.

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Authors and Affiliations

  1. College of Computer Science & Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, China

    Guojia Hou, Jingming Li, Guodong Wang, Zhenkuan Pan & Xin Zhao

  2. School of Automation, Qingdao University, No. 308 Ningxia Road, Qingdao, China

    Guojia Hou

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  1. Guojia Hou
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  2. Jingming Li
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Correspondence to Guojia Hou.

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Hou, G., Li, J., Wang, G. et al. Underwater image dehazing and denoising via curvature variation regularization. Multimed Tools Appl 79, 20199–20219 (2020). https://doi.org/10.1007/s11042-020-08759-z

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  • DOI: https://doi.org/10.1007/s11042-020-08759-z

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