An underwater polarimetric image descattering and material identification method based on unpaired multi-scale polarization fusion adversarial generative network

Liyang Wu; Xiaofang Zhang

doi:10.1117/12.3018076

30 April 2024 An underwater polarimetric image descattering and material identification method based on unpaired multi-scale polarization fusion adversarial generative network

Liyang Wu, Xiaofang Zhang

Author Affiliations +

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 1315613 (2024) https://doi.org/10.1117/12.3018076
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

Underwater optical imaging technology is extremely important for a wide range of applications, e.g., the operation and rescue, resources exploration and species protection in complex underwater environment. However, the underwater images always suffer from reduction of intensity, loss of contrast and blurred details due to the scattering and absorption caused by the scattering medium. Therefore, effective research methods are needed to achieve clear underwater imaging. In this paper, we propose an underwater polarimetric image descattering and material identification method based on unpaired multi-scale polarization fusion adversarial generative network. In our method, polarimetric image with different polarization states (I₀, I₄₅, I₉₀) and the degree of polarization (DOP) maps are introduced to the network for the purpose of fully extracting polarization information. Experimental results show that our method can remove the underwater scattering effect effectively. And good restoration effect is achieved under unparied data sets, which breaks the dependence on strictly paired polarimetric images in previous learning methods. Not only complete intensity information is retained, but also more polarization information of the target could be extracted from the descattered image, which is especially beneficial for subsequent target identification. The average values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the descattered images under different turbidity have been greatly improved comparing with the original images. All in all, excellent performance is achieved in both subjective visual perception and objective evaluation indicators using our method, which provides a new way for underwater polarization imaging.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Liyang Wu and Xiaofang Zhang "An underwater polarimetric image descattering and material identification method based on unpaired multi-scale polarization fusion adversarial generative network", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 1315613 (30 April 2024); https://doi.org/10.1117/12.3018076

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KEYWORDS

Polarization

Turbidity

Image restoration

Polarization imaging

Feature extraction

Polarized light

Scattering

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