Image-restoration algorithm based on an underwater polarization imaging visualization model

The polarization bidirectional reflection distribution function theory of a target is combined with microfacet theory, and the Monte Carlo method is used to establish an underwater laser active-polarization imaging model based on Mie scattering theory. The model presented herein can simulate imaging of an underwater target with a high degree of polarization, and the effects of optical thickness and target surface roughness on active underwater laser imaging results are demonstrated by the simulation image. Combined with histogram equalization and the traditional polarization differential imaging algorithm, an algorithm is presented herein that globally estimates the mutual information value between the target polarization degree and the correction factor of backscattered light polarization degree. The images received from the simulation test can be restored, and results show that the algorithm can restore the target image with a high degree of polarization to some extent. Finally, the correctness of the active underwater laser polarization imaging model and the feasibility of global estimation based on the polarization differential restoration algorithm are verified experimentally. ?? 2022 Optica Publishing Group

Automated summary

This paper combines the polarization bidirectional reflection distribution function theory with microfacet theory and uses Monte Carlo method to establish an underwater laser active-polarization imaging model based on Mie scattering theory. The model can simulate imaging of an underwater target with a high degree of polarization, and demonstrates the effects of optical thickness and target surface roughness on the imaging results. Additionally, the paper presents an algorithm that globally estimates the mutual information value between the target polarization degree and the correction factor of backscattered light polarization degree, using histogram equalization and the traditional polarization differential imaging algorithm. The algorithm is able to restore the highly polarized target image to some extent, as shown by the simulation results. Finally, the experimental verification confirms the correctness of the imaging model and the feasibility of the global estimation algorithm based on polarization differential restoration.