Underwater imaging plays a critical role in marine rescue, seabed resource exploration, underwater archaeology, etc. by providing human-vision-system-friendly information. A variety of approaches have been exploited to realize clear underwater imaging. Noticeably, underwater polarization imaging has attracted attention due to its simple imaging system and clear vision. It can remove the backscattered light from degraded image and recover abundant high-fidelity information of target. Descattering is conducted by using the difference in polarization characteristics between the target and background. A classical underwater polarization imaging method is presented by Schechner [Tali T, Schechner Y Y
2009
IEEE Trans. Pattern Anal. Mach. Intell.
31385
], in which the differential polarization characteristics of backscattered light and target light are used to recover clear image. More researches were conducted including Huang et al.’s research [Huang B J, Liu T G, Hu H F, et al.
2016
Optics Express
249826
], Liu et al.’s study [Liu F, Han P L, Wei Y, et al.
2018
Opt. Lett.
434903
], etc.
However, in the polarization imaging methods, the uniform underwater backscattered light and polarization parameters over the whole image are usually assumed. In most practical applications, these assumptions cannot hold true. Therefore, the inaccurate estimation of backscattered light makes it difficult to completely descatter an image, leading many methods to fail to detect the target in non-uniform turbid water.
In this study, we propose a low-rank-and-sparse-decomposition-based polarization imaging combined with common mode rejection feature of polarization information in scattered light field to eliminate non-uniformity and scattering caused by severe scattering during active polarization imaging of turbid water. The backscattered light is highly reduced and the information contained in background is single and highly correlated. It conforms to the low-rank characteristics of the image. What is more, the target in underwater scene occupies a relatively small proportion, which conforms to the sparsity characteristics of the image. Therefore, combining the low-rank characteristics of backscattered light with the sparse characteristics of target information light, we separate them through low-rank and sparse matrix decomposition to recover clear underwater image. Both experimental and objective image quality evaluation results demonstrate the validity of the proposed method.
The proposed method works well in improving polarization vision in non-uniform turbid water, which is due to its ability to make the underwater scene uniform and the target and background information separated through their distribution difference of polarization characteristics. It possesses potential applications in turbid water imaging.
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