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

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.