An improved two-threshold quantum segmentation algorithm for NEQR image

• URL: http://arxiv.org/abs/2311.12033v1
• Date: Mon, 2 Oct 2023 17:04:36 GMT
• Title: An improved two-threshold quantum segmentation algorithm for NEQR image
• Authors: Lu Wang, Zhiliang Deng, Wenjie Liu
• Abstract summary: An improved two-threshold quantum segmentation algorithm for NEQR image is proposed. For a 2(n)*2(n) image with q-scale levels, the quantum cost of our algorithm can be reduced to 60q-6.
• Score: 6.601450061692404
• License: http://creativecommons.org/licenses/by/4.0/
• 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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