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Resolution enhancement for fiber bundle imaging using maximum a posteriori estimation.

Jianbo Shao, Wei-Chen Liao, Rongguang Liang

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    PubMed
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    This study introduces a new framework for coherent fiber bundle imaging, enhancing spatial resolution and removing fixed patterns. The method achieves a 2.8x resolution improvement using principled forward modeling and maximum a posteriori estimation.

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    Area of Science:

    • Optics and Imaging
    • Computational Imaging
    • Image Reconstruction

    Background:

    • Coherent fiber bundle imaging systems often suffer from limited spatial resolution and fixed structural patterns.
    • Existing methods may not effectively address both resolution enhancement and pattern removal simultaneously.

    Purpose of the Study:

    • To develop a novel framework for joint spatial resolution improvement and fixed pattern removal in coherent fiber bundle imaging.
    • To leverage principled forward modeling and maximum a posteriori (MAP) estimation for enhanced image reconstruction.

    Main Methods:

    • A principled forward model was developed, mapping high-resolution representations to low-resolution images considering probe motions and point spread functions.
    • A smoothing prior was incorporated into the forward model.
    • Maximum a posteriori (MAP) estimation was computed using conjugate gradient descent to reconstruct high-resolution images from low-resolution inputs.
    • The framework supports various applicable transformations.

    Main Results:

    • The proposed method successfully enhanced spatial resolution and removed fixed structural patterns.
    • On USAF target data with 16 low-resolution captures, a spatial resolution enhancement factor of 2.8 was achieved.
    • Validation was performed on synthesized data, a transmissive USAF target, and lens tissue data.

    Conclusions:

    • The developed framework offers a robust approach for improving image quality in coherent fiber bundle imaging systems.
    • Principled forward modeling and MAP estimation provide a powerful combination for addressing limitations in this imaging domain.