Polarization Reconstruction Algorithm of Target Based on the Analysis of Noise in Complex Underwater Environment - Song, Qiang; Liu, Xiao; Huang, Honglian; Ti, Rufang; Sun, Xiaobing (Frontiers in Physics, 2022)
How to effectively eliminate water interference such as scattering, absorption and attenuation is a hot topic of underwater photoelectric detection at present. Around the hot issues, this article carries out studying methods of polarization-imaging recovery in complex underwater environment from the theory of underwater radiation transfer, numerical simulation of imaging interference characteristics and simulation of underwater environment experiment. First of all, conducting the analysis and simulation of scattering characteristics of underwater suspension particles and turbid medium by using the theory of radiation transfer, taking advantage of quantitative description on changing tendency of radiation intensity and polarization properties of light waves in turbid water under the condition of forward and backward scattering interference; Secondly, coupling influence factors of target polarization characteristics into global scene reconstruction on the basis of classical Yoav’s model of underwater target polarization reconstruction, automatically estimating target’s polarization information by the method of covariance, using multi-scale filtering to promote target’s resolution of reconstruction results and eventually improve reconstruction accuracy of underwater scene; Finally, building multi-spectral polarization imaging system in the simulated complex underwater environment that contains bubble and suspended particles, obtaining restored results with different wavebands and different materials of targets in the underwater environment. According to experiment results, it is concluded that: compared with other traditional methods of underwater scene reconstruction, using the method proposed in this article can get higher resolution and higher contrast of target in the reconstructed result, and obtaining same conclusions by using evaluation index of information entropy, average gradient, edge strength and variance. Expecting the method of underwater imaging has important application value especially in artificial underwater object recognition.