Local optimum underwater polarization imaging enhancement based on connected domain prior

Abstract

Estimating the degree of polarization (DOP) of target signal light () is a critical step in the underwater polarization differential imaging process. It is challenging to accurately estimate the of objects containing multiple attributes in the imaging range. Inspired by the optical polarization characteristics of objects with different DOP, we find a prior of connected domains (CDs) of objects in polarization orthogonal images. Based on this prior, the CDs of low-DOP (LDOP) and high-DOP (HDOP) objects are extracted from the darkest image () and brightest image () without additional image acquisition. The of LDOP and HDOP objects in the scene can be estimated through the extracted CDs, which realizes the transition from global optimum estimation in the traditional method to local optimum estimation. According to the estimated , the enhanced results of the entire image marked by can be obtained after fusing the enhanced results of the objects in each CD. Experimental results of simple and complex combination scenes of objects show that our method can recognize CDs of LDOP and HDOP objects. We obtained the enhanced results of the entire image, verified the feasibility and robustness of our method, and found that the CDs of LDOP and HDOP objects could be effectively extracted in up to NTU of turbid water. The local optimum improves the application ability of underwater polarization imaging in unpredictable scenarios, while the enhanced result of target labeling has potential application value in underwater target recognition and classification.