Two-dimensional polarization distance underwater imaging method inspired by biomimetic vision

Polarization imaging can obtain multi-dimensional information, demonstrating significant advantages in underwater application scenarios. Many organisms rely on polarized vision systems to capture subtle polarization information differences to achieve the purposes of avoiding natural enemies, foraging and navigation. The current underwater polarization imaging algorithms focus on using polarization information to enhance image details, but the practical difficulty is to effectively obtain and use underwater polarization information and reduce the estimation error of background scattered light. Inspired by polarization vision and color vision, this paper proposes a two-dimensional polarization distance model. The model combines the degree of polarization (DOP), angle of polarization (AOP) and other parameters to quantify the polarization difference between the target and the background as a distance in the mathematical space, so as to evaluate the difference of polarization characteristics between the two. In addition, through frequency domain analysis and polarization state reconstruction, the global distribution of AOP and DOP of background scattered light is obtained, which can effectively reduce the estimation error of background scattered light. The results show that this method can effectively improve the contrast between target and background in turbid water, and demonstrates good visual effect.