A quantum segmentation algorithm based on local adaptive threshold for NEQR image

• URL: http://arxiv.org/abs/2311.11953v1
• Date: Mon, 2 Oct 2023 04:01:42 GMT
• Title: A quantum segmentation algorithm based on local adaptive threshold for NEQR image
• Authors: Lu Wang, Wenjie Liu
• Abstract summary: The complexity of our algorithm can be reduced to $O(n2+q)$, which is an exponential speedup compared to the classic counterparts. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
• Score: 7.798738743268923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a $2^n\times 2^n$ image with q gray-scale levels, the complexity of our algorithm can be reduced to $O(n^2+q)$, which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

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