An improved two-threshold quantum segmentation algorithm for NEQR image
This work addresses quantum resource efficiency for quantum image processing, but it is incremental as it builds upon existing NEQR methods with specific optimizations.
The paper tackles the problem of high quantum resource usage in quantum image segmentation by proposing an improved two-threshold algorithm for NEQR images, which reduces quantum cost to 60q-6 and effectively segments complex gray-scale images as demonstrated on IBM Q.
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.