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Edge detection based on joint iteration ghost imaging.

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    This study introduces a novel ghost imaging method for faster, higher-quality edge detection. The technique successfully recovers object details from random patterns with reduced measurement times, enhancing practical applications in security and medicine.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Computational Imaging

    Background:

    • Edge detection is crucial for security checking and medical diagnosis.
    • Traditional ghost imaging for edge detection demands extensive measurement times and lacks direct target imaging.
    • Existing methods struggle with efficiency and direct image reconstruction in ghost imaging applications.

    Purpose of the Study:

    • To develop an improved ghost imaging method for enhanced edge detection quality.
    • To reduce measurement times required for ghost imaging.
    • To enable direct, high-quality imaging alongside edge detection.

    Main Methods:

    • Proposed a novel method combining projected Landweber iteration regularization with guided filter ghost imaging.
    • Employed joint iteration for improved feature detection and image reconstruction.
    • Validated through simulations and experimental setups without special coding.

    Main Results:

    • Successfully recovered spatial and edge information from random speckle patterns.
    • Achieved high-quality imaging with significantly reduced measurement times.
    • Demonstrated remarkable improvement in edge image quality compared to existing methods.

    Conclusions:

    • The proposed joint iteration ghost imaging method enhances edge detection quality and imaging fidelity.
    • This approach overcomes limitations of traditional ghost imaging, reducing measurement time.
    • The method broadens the applicability of ghost imaging in practical fields like security and medical diagnosis.