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Quantum image processing algorithm using edge extraction based on Kirsch operator.

Pengao Xu, Zhenxing He, Tianhui Qiu

    Optics Express
    |May 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a quantum image processing algorithm for edge extraction using the Kirsch operator and novel enhanced quantum representation (NEQR). The quantum approach offers significantly faster and more accurate real-time image processing than classical methods.

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    Area of Science:

    • Quantum Computing
    • Image Processing
    • Computer Vision

    Background:

    • Classical edge extraction algorithms face limitations in speed and accuracy for real-time applications.
    • Quantum image processing offers potential for enhanced computational efficiency.

    Purpose of the Study:

    • To develop and analyze a novel quantum image processing algorithm for edge extraction.
    • To leverage the Kirsch operator within a quantum framework for improved image analysis.

    Main Methods:

    • Utilized novel enhanced quantum representation (NEQR) for quantum image encoding.
    • Implemented the Kirsch operator for edge detection on quantum images.
    • Designed, analyzed, and simulated quantum circuits for the proposed algorithm.

    Main Results:

    • The developed quantum algorithm successfully performs edge extraction using the Kirsch operator.
    • Simulations demonstrate significantly higher accuracy and speed compared to classical edge extraction algorithms.
    • The approach enables real-time quantum image processing.

    Conclusions:

    • The proposed quantum image processing algorithm provides a superior alternative to classical methods for edge extraction.
    • NEQR combined with the Kirsch operator in a quantum circuit offers a promising direction for efficient image analysis.
    • This work highlights the potential of quantum computing for accelerating and enhancing image processing tasks.