An improved two-threshold quantum segmentation algorithm for NEQR image
Lu Wang, Zhiliang Deng, Wenjie Liu
TL;DR
The paper tackles the need for low-qubit, scalable quantum image segmentation by introducing an improved two-threshold segmentation algorithm for NEQR images. It presents a low-cost quantum comparator and a scalable circuit capable of partitioning grayscale images into $n+1$ segments with a total quantum cost of $60q-6$, independent of image size. The method is demonstrated on IBM Q simulators, showing clearer ternary segmentation compared to existing threshold-based approaches. The work advances practical QIP by enabling more detailed segmentation with fewer quantum resources, while acknowledging limitations for dynamic scenes and outlining plans to extend to higher thresholds. Overall, the approach offers a resource-efficient path toward multilevel quantum image segmentation in the NISQ era.
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.