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    Simultaneous optimization of coded aperture snapshot spectral imaging (CASSI) systems significantly enhances hyperspectral image reconstruction quality and sampling efficiency. This approach minimizes sensing matrix coherence, improving peak signal-to-noise ratio by over 5dB.

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    Area of Science:

    • Computational Imaging
    • Hyperspectral Imaging
    • Compressive Sensing

    Background:

    • Coded aperture snapshot spectral imaging (CASSI) reconstructs hyperspectral images using compressive sensing.
    • Reconstruction quality and sampling efficiency depend on the sensing matrix coherence.
    • Previous efforts individually optimized coded apertures or sparse bases.

    Purpose of the Study:

    • To introduce a simultaneous optimization method for system projection and dictionary to minimize Frobenius norm coherence.
    • To improve CASSI reconstruction quality and sampling efficiency through reduced matrix coherence.

    Main Methods:

    • Adopted a dual-disperser structure and RGB image sensor for low system coherence.
    • Optimized coded aperture using genetic algorithm and dictionary using gradient descent.
    • Performed iterative simultaneous optimization of system projection and dictionary.

    Main Results:

    • Achieved significantly lower sensing matrix coherence.
    • Demonstrated substantial improvements in hyperspectral image reconstruction quality and sampling efficiency.
    • Increased peak signal-to-noise ratio by over 5dB compared to non-optimized and individually optimized systems.

    Conclusions:

    • Simultaneous optimization of system matrix and sparse basis effectively minimizes coherence.
    • This method offers significant performance gains for CASSI systems.
    • The approach holds potential for advancing other compressive-sensing-based imaging systems.