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    Compressive sensing enables spectral imaging via coded aperture snapshot spectral imagers (CASSI). This study enhances CASSI by incorporating sensor spectral sensitivity for improved color image reconstruction quality.

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    Area of Science:

    • Optics and photonics
    • Signal processing
    • Computational imaging

    Background:

    • Compressive sensing (CS) is a signal processing technique for reconstructing high-dimensional signals from limited measurements.
    • The coded aperture snapshot spectral imager (CASSI) system uses CS for single-snapshot multispectral imaging.
    • Existing CASSI systems primarily use monochrome sensors, limiting color information capture.

    Purpose of the Study:

    • To expand the theoretical framework of CASSI to include image sensor spectral sensitivity for accurate color data acquisition.
    • To investigate the impact of different sensor types on multispectral image quality within the CASSI framework.

    Main Methods:

    • Developed an enhanced theoretical model for CASSI incorporating spectral sensitivity.
    • Evaluated image quality using a traditional color filter array (CFA) sensor.
    • Assessed image quality using a stacked-pixel Foveon image sensor.

    Main Results:

    • The inclusion of spectral sensitivity significantly impacts CASSI performance.
    • Foveon sensors, with their stacked pixel design, offer potential advantages over traditional CFA sensors for CASSI color reconstruction.
    • Quantitative analysis demonstrated differences in reconstructed multispectral data cubes based on sensor type.

    Conclusions:

    • Accounting for sensor spectral sensitivity is crucial for accurate color multispectral imaging with CASSI.
    • The choice of image sensor architecture critically influences the quality of reconstructed multispectral images.
    • This research provides a foundation for optimizing CASSI systems for advanced color spectral imaging applications.