Local optimum underwater polarization imaging enhancement based on connected domain prior

Abstract Estimating the degree of polarization (DOP) of target signal light ( <?CDATA ${p_{{\text{targ}}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>targ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) is a critical step in the underwater polarization differential imaging process. It is challenging to accurately estimate the <?CDATA ${p_{{\text{targ}}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>targ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> 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 ( <?CDATA ${I_{\min }}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mi>min</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) and brightest image ( <?CDATA ${I_{\max }}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mo form="prefix" movablelimits="true">max</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) without additional image acquisition. The <?CDATA ${p_{{\text{targ}}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>targ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> 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 <?CDATA ${p_{{\text{targ}}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>targ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , the enhanced results of the entire image marked by <?CDATA ${p_{{\text{targ}}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>targ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> 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 <?CDATA $34.3$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>34.3</mml:mn> </mml:math> 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.