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Updated: Jul 16, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS.

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    This study introduces a new infrared image correction method that effectively removes non-uniformity while adapting to different scenes. The algorithm balances strong correction with scene adaptability, improving overall image quality.

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    Area of Science:

    • Infrared imaging technology
    • Image processing and computer vision

    Background:

    • Non-uniformity in infrared detector output images significantly degrades image quality.
    • Existing algorithms struggle to balance strong non-uniformity correction with scene adaptability.

    Purpose of the Study:

    • To propose a novel scene-based algorithm for infrared non-uniformity correction.
    • To address the limitations of existing methods in balancing correction strength and scene adaptability.

    Main Methods:

    • A novel scene-based algorithm combining single-frame stripe removal, multi-scale statistics, and least mean square (LMS) methods.
    • A coarse-to-fine correction process utilizing adaptive progressive correction with Laplacian pyramids.
    • Improved 1-D guided filtering and high-pass filtering for non-uniformity separation.
    • Optimized expected image estimation and spatio-temporal adaptive learning rates using guided filtering LMS.

    Main Results:

    • The proposed algorithm effectively separates non-uniformity from scene content by shaping high-frequency sub-bands.
    • Ghosting artifacts are significantly suppressed.
    • Extensive simulations and real experiments validate the algorithm's adaptability and effectiveness in correcting strong non-uniformity.

    Conclusions:

    • The novel algorithm demonstrates superior performance in infrared non-uniformity correction compared to existing methods.
    • It offers a robust solution for improving infrared image quality across diverse scenarios.