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Quantum implementation of classical Marr–Hildreth edge detection

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Abstract

Edge detection is a fundamental task in digital image processing. Marr–Hildreth edge detection is the basic tool for implementing edge detection in classic image processing. This paper studies the quantum version of the classical Marr–Hildreth edge detection, which includes two core processes: Gaussian–Laplacian filtering and zero-crossing extraction. Based on the sampling results of the Gaussian–Laplace function, Gaussian–Laplacian filtering is implemented directly using quantum multipliers and quantum adders. Zero-crossing extraction is achieved using several quantum comparators. The quantum circuits of these two core processes with several auxiliary operators are designed in detail. Complexity analysis shows that the quantum Marr–Hildreth edge detection has exponential speedup compared to its classical counterpart. The simulation on the classical computer verifies the correctness of the quantum Marr–Hildreth edge detection results.

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Acknowledgements

This work was supported by the Youth Science Foundation of Northeast Petroleum University (Grant No. 2018QNL-08), and the Guiding Innovation Fund of Northeast Petroleum University (Grant No. 2018YDL-20).

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  1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, China

    Panchi Li, Tong Shi, Aiping Lu & Bing Wang

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  1. Panchi Li
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Correspondence to Panchi Li.

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Li, P., Shi, T., Lu, A. et al. Quantum implementation of classical Marr–Hildreth edge detection. Quantum Inf Process 19, 64 (2020). https://doi.org/10.1007/s11128-019-2559-0

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