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    This study introduces a novel polarization-informed deep learning method for restoring degraded polarimetric images. The approach effectively recovers Stokes parameters and degree of linear polarization in challenging environments like turbid water.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Machine Learning

    Background:

    • Polarimetric imaging captures light polarization information, crucial for understanding material properties and environmental conditions.
    • Degradations like scattering, attenuation, and occlusion in turbid media severely impair polarimetric image quality.
    • Recovering accurate polarimetric data (Stokes parameters, degree of linear polarization) is essential for reliable analysis.

    Purpose of the Study:

    • To develop and evaluate a novel polarimetric image restoration approach using polarization-informed deep learning and 3D integral imaging.
    • To recover degraded Stokes parameters and the degree of linear polarization.
    • To estimate the Mueller matrix for characterizing transmission media and objects.

    Main Methods:

    • Utilized an unsupervised image-to-image translation (UNIT) framework for Stokes parameter restoration.
    • Employed a multi-output convolutional neural network (CNN) for Mueller matrix estimation.
    • Integrated 3D integral imaging to mitigate degradations in turbid media.
    • Evaluated performance under varying turbidity and partial occlusion conditions.

    Main Results:

    • The proposed method successfully recovered Stokes parameters and the degree of linear polarization from degraded images.
    • Mueller matrix estimation provided insights into transmission media and object characteristics.
    • 3D integral imaging demonstrated effectiveness in reducing turbidity-induced degradations.
    • Experimental results confirmed the approach's promise under diverse environmental degradations.

    Conclusions:

    • The developed polarization-informed deep learning approach shows significant promise for polarimetric image restoration in degraded environments.
    • This work represents the first application of polarization-informed deep learning within 3D imaging for recovering polarimetric information and Mueller matrix estimates.
    • The method offers a robust solution for applications requiring accurate polarimetric analysis in challenging conditions.