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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Computational Imaging

    Background:

    • Coded aperture compressive temporal imaging (CACTI) enables high-speed photography using low-speed cameras by encoding multiple frames into a single measurement.
    • Both the encoding pattern and reconstruction algorithm are critical for CACTI performance.
    • Existing methods face limitations in reconstruction quality.

    Purpose of the Study:

    • To enhance the reconstruction quality of CACTI.
    • To develop an improved encoding strategy and a novel reconstruction algorithm for CACTI.

    Main Methods:

    • Utilized the reflective properties of a digital micromirror device to implement a complementary dual-mask encoding pattern for richer projection information.
    • Developed a novel reconstruction algorithm combining weighted Landweber regularization, a relaxation strategy, and a deep denoiser.
    • Conducted experimental validation to assess the performance of the proposed encoding-decoding combination.

    Main Results:

    • The proposed complementary dual-mask pattern effectively captured more projection information.
    • The combined weighted Landweber regularization, relaxation strategy, and deep denoiser algorithm demonstrated superior reconstruction capabilities.
    • Experimental results showed significant improvements in peak Signal-to-Noise Ratio (SNR), structural similarity index measure (SSIM), and visual quality compared to existing methods.

    Conclusions:

    • The developed encoding-decoding strategy significantly improves CACTI performance.
    • The novel approach offers a promising solution for high-speed imaging applications requiring high-quality reconstructions.
    • This work advances the field of compressive imaging by enhancing both data acquisition and signal recovery.