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Quantum Information Processing

Erschienen in:

01.10.2022

An adaptive threshold-based quantum image segmentation algorithm and its simulation

verfasst von: Suzhen Yuan, Wenhao Zhao, Shengwei Gao, Shuyin Xia, Bo Hang, Hong Qu

Erschienen in: Quantum Information Processing | Ausgabe 10/2022

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Abstract

Efficient and accurate image segmentation algorithm is critical to image processing. In this paper, we design a quantum image segmentation algorithm utilizing an adaptive threshold based on a moving average method, and we simulate it on the IBM Quantum Experience (IBM Q) platform through the Qiskit extension. In the proposed method, an image is first divided into many 2Œ2 regions, and each region’s average value is considered the region’s threshold value. In order to fully exploit quantum parallelism, we encode the core image (image to be segmented) and the three auxiliary images into one quantum superposition state sharing the same position qubits. The analysis results highlight that the proposed quantum image segmentation algorithm provides exponential speedup over the existing implementations, and the number of auxiliary qubits is reduced from exponential of q to polynomial. In addition, this paper presents an appealing example of simulating complex quantum image processing algorithms in quantum simulators.

Vorheriger Artikel Quantum classification algorithm with multi-class parallel training

Nächster Artikel A new post-quantum multivariate polynomial public key encapsulation algorithm

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Titel: An adaptive threshold-based quantum image segmentation algorithm and its simulation
verfasst von: Suzhen Yuan
Wenhao Zhao
Shengwei Gao
Shuyin Xia
Bo Hang
Hong Qu
Publikationsdatum: 01.10.2022
Verlag: Springer US
Erschienen in: Quantum Information Processing / Ausgabe 10/2022
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI: https://doi.org/10.1007/s11128-022-03709-0

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