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High-quality compressed sensing imaging with limited detector bits using sparse measurements and multiple dithers.

Fan Liu, Xue-Feng Liu, Xu-Ri Yao

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    Summary

    This study introduces an improved compressed sensing (CS) imaging method using sparse measurements and dithering. The technique enhances image quality by reducing quantization errors, potentially enabling 1-bit detector systems.

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    Area of Science:

    • Image Reconstruction
    • Signal Processing
    • Compressed Sensing Imaging

    Background:

    • Compressed sensing (CS) imaging systems are susceptible to quantization disturbances due to high-flux measurements.
    • Limited detector bits in CS imaging systems can compromise image quality.
    • Quantization errors significantly impact the accuracy of reconstructed images in CS.

    Purpose of the Study:

    • To develop a high-quality CS imaging method for systems with limited detector bits.
    • To mitigate reconstruction errors caused by quantization distortions.
    • To reduce the required number of detector bits for effective CS imaging.

    Main Methods:

    • Proposed an improved imaging method combining sparse measurements and multiple dithers.
    • Reduced the dynamic range of measured signals.
    • Increased the dynamic range of effective detection.

    Main Results:

    • The proposed system significantly decreases reconstruction errors caused by quantization distortions compared to traditional CS imaging.
    • Demonstrated the feasibility of reducing the required number of detector bits to as low as 1.
    • Simulations and experiments validated the method's performance and feasibility.

    Conclusions:

    • The novel CS imaging approach effectively addresses quantization issues in high-flux measurements.
    • This method enables high-quality CS imaging with minimal detector bits, enhancing system efficiency.
    • Further analysis of detector noise and system parameters confirms the technique's practical applicability.