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arXiv · 2311.12033

An improved two-threshold quantum segmentation algorithm for NEQR image

Lu Wang·Zhiliang Deng·Wenjie Liu
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Abstract

The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an improved two-threshold quantum segmentation algorithm for NEQR image is proposed, which can segment the complex gray-scale image into a clear ternary image by using fewer qubits and can be scaled to use n thresholds for n + 1 segmentations. In addition, a feasible quantum comparator is designed to distinguish the gray-scale values with two thresholds, and then a scalable quantum circuit is designed to segment the NEQR image. For a 2^(n)*2^(n) image with q gray-scale levels, the quantum cost of our algorithm can be reduced to 60q-6, which is lower than other existing quantum algorithms and does not increase with the image's size increases. The experiment on IBM Q demonstrates that our algorithm can effectively segment the image.

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Lu Wang, Zhiliang Deng, Wenjie Liu. 2023-10-02. An improved two-threshold quantum segmentation algorithm for NEQR image. https://doi.org/10.1007/s11128-022-03624-4

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