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Laplace dark channel attenuation-based single image defogging in ocean scenes

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Abstract

Single image defogging is of paramount importance for the perception and recognition tasks of maritime targets since fog can severely degrade the content and details of ocean images. This paper studies the less-touched defogging problem, namely, how to remove the fog in ocean scene images. The physical characteristics of ocean scenes are different from those of land scenes. Some existing methods can not work effectively when processing sea fog images, which are induced by transmission value deviation or atmospheric light misestimation. Motivated by this, a Laplace dark channel attenuation defogging method for sea fog images is proposed. Specifically, a Laplace function attenuation minimum channel is constructed to obtain the Laplace dark channel, which can reduce the transmission value deviation in different regions. Meanwhile, to avoid atmospheric light estimation being affected by the large areas of white objects in sea fog images, an atmospheric light estimation method based on V-component direct current (DC) coefficient block is proposed. Moreover, the glow effect, which is ignored by many defogging methods, is taken into account. A Gaussian function of the brightness component is constructed to suppress the glow-shaped halo effect in the defogged images. The GMSD, σ, PSNR, and SSIM obtained by our method are 0.036, 0.805, 18.20, and 0.87, respectively. Experimental results show that the proposed defogging method, which effectively improves the poor defogging performance of most existing defogging methods in ocean scenes, can achieve favorable performance compared with the state-of-the-art defogging methods in both qualitative and quantitative comparisons.

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Data Availability

The datasets URHI and HSTS analysed during this study are included in the article [24] and its supplementary information files (https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=0). The NYU2 dataset analysed during this study are included in the article [36] and its supplementary information files (https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html). The datasets collected by us during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Science Foundation of China (Grant Nos. 61873335, 61833011, 62103251); the Project of Science and Technology Commission of Shanghai Municipality, China (Grant Nos. 20ZR1420200, 21SQBS01600, 22JC1401400, 19510750300, 21190780300); the 111 Project, China (Grant No. D18003); and the China Postdoctoral Science Foundation (Grant No. 2021M702075).

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Zi-Xin Li, Yu-Long Wang, Chen Peng & Yan Peng

  2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, 200444, China

    Zi-Xin Li, Yu-Long Wang & Chen Peng

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  1. Zi-Xin Li
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Correspondence to Yu-Long Wang.

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Li, ZX., Wang, YL., Peng, C. et al. Laplace dark channel attenuation-based single image defogging in ocean scenes. Multimed Tools Appl 82, 21535–21559 (2023). https://doi.org/10.1007/s11042-022-14103-4

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