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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Computer Vision

    Background:

    • Passive polarization imaging utilizes degree and angle of linear polarization for detecting polarized objects.
    • Polarized signals are often near sensor noise limits (CCD, CMOS), degrading with under-exposure.
    • Existing methods struggle with noise sensitivity in polarization imaging.

    Purpose of the Study:

    • To enhance the robustness of polarization imaging against sensor noise.
    • To introduce a novel method for estimating noise-free Stokes simplex vectors.
    • To improve the detection of small polarized objects in noisy scenes.

    Main Methods:

    • Developed the Stokes Simplex Polarimetric Image Denoising (SSPID) algorithm.
    • Estimated Cartesian coordinates of Stokes simplex vectors in the wavelet domain.
    • Utilized minimum mean squared error estimation for Poisson corrupted analyzer images.

    Main Results:

    • SSPID demonstrated superior performance compared to state-of-the-art methods.
    • The algorithm effectively denoises polarization images corrupted by sensor noise.
    • Benchmarking was conducted on division-of-time (DoT) polarimetric data.

    Conclusions:

    • The proposed SSPID algorithm significantly improves polarization image denoising.
    • Stokes simplex estimation offers a robust approach to handle sensor noise.
    • This work advances the capability of detecting polarized objects in challenging conditions.