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Single exposure lensless subpixel phase imaging: optical system design, modelling, and experimental study.

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    This study introduces a novel lensless optical system for super-resolution imaging using a single observation and advanced phase retrieval. The system successfully reconstructs subpixel details, significantly surpassing traditional resolution limits.

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    Area of Science:

    • Optical Engineering
    • Image Processing
    • Computational Imaging

    Background:

    • Traditional super-resolution techniques often require multiple observations or complex optical setups.
    • Phase retrieval in optics is crucial for reconstructing wavefront information from intensity measurements.

    Purpose of the Study:

    • To design and optimize a lensless phase-retrieval optical system for subpixel imaging and super-resolution reconstruction.
    • To develop a single-observation method that overcomes limitations of conventional super-resolution phase retrieval.

    Main Methods:

    • Utilized phase modulation of free-space propagation wavefront.
    • Employed the Super-Resolution Sparse Phase Amplitude Retrieval (SR-SPAR) iterative technique with optimized sparsity and multi-scale filters.
    • Applied a random phase-mask to modulate the object wavefront, generating a coded diffracted intensity pattern.

    Main Results:

    • Achieved high-quality super-resolution reconstructions with a factor of 5 in simulations, acceptable up to a factor of 9.
    • Demonstrated physical experimental resolution of 3 μm details, exceeding the Nyquist-Shannon limit by 2.3 times.
    • Verified the system's noise-robustness through investigation and experimentation.

    Conclusions:

    • The proposed lensless phase-retrieval system enables efficient super-resolution imaging with a single observation.
    • The SR-SPAR technique effectively extracts subpixel information for enhanced image reconstruction.
    • The system offers a practical approach to achieving super-resolution beyond conventional optical limitations.