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Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal |
Bo WANG1(), Zitong KANG1, Pengwei DONG1, Fan WANG1, Peng MA1, Jiajing BAI1, Pengwei LIANG1, Chongyi LI2 |
1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China 2. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore |
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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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Keywords
underwater image enhancement
adaptive color correction
background light estimation
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Corresponding Author(s):
Bo WANG
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Just Accepted Date: 19 October 2021
Issue Date: 17 March 2022
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