4.6 Review

Polarimetric Imaging Through Scattering Media: A Review

Journal

FRONTIERS IN PHYSICS
Volume 10, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fphy.2022.815296

Keywords

polarization; polarimetric imaging; scattering media; imaging recovery; physical imaging

Funding

  1. National Natural Science Foundation of China [62075161, 61775163]
  2. HKSAR Research Grants Council, General Research Fund [14206517]
  3. Innovation and Technology Commission, Innovation Technology Fund [ITS/428/17FP]
  4. Guangxi Innovation-Driven Development Project [GuikeAA21077008]

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This study reviews the recent development in polarimetric imaging techniques to address the challenges in imaging in scattering media. These techniques can estimate the intensity level and transmittance of the backscattering light, separate target signals from undesired ones, and recover high-quality images using additional metrics provided by different designs of polarimetric imaging systems.
Imaging in scattering media has been a challenging and important subject in optical science. In scattering media, the image quality is often severely degraded by the scattering and absorption effects owing to the small particles and the resulting nonuniform distribution of the intensity or polarization properties. This study reviews the recent development in polarimetric imaging techniques that address these challenges. Specifically, based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. They can also separate the target signal from the undesired ones to achieve high-quality imaging. In addition, the different designs of the polarimetric imaging systems offer additional metrics, for example, the degree/angle of polarization, to recover images with high fidelity. We first introduce the physical degradation models in scattering media. Secondly, we apply the models in different polarimetric imaging systems, such as polarization difference, Stokes vector, Mueller matrix, and deep learning-based systems. Lastly, we provide a model selection guideline and future research directions in polarimetric imaging.

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