A quantum segmentation algorithm based on background-difference method for NEQR image
Lu Wang, Wenjie Liu, Zhiliang Deng · Quantum Information and Computation · 2023
Quantum image segmentation algorithm can use its quantum mechanism to rapidly segment the objects in a quantum image. However, the existing quantum image segmentation algorithms can only segment static objects in the image and use more quantum resource(qubit). In this paper, a novel quantum segmentation algorithm based on background-difference method for NEQR image is proposed, which can segment dynamic objects in a static scene image by using fewer qubits. In addition, an efficient and feasible quantum absolute value subtractor is designed, which is an exponential improvement over the existing quantum absolute value subtractor. Then, a complete quantum circuit is designed to segment the NEQR image. For a ${2^n}$$\times$${2^n}$ image with gray-scale range of [0,$2^q$-1], the complexity of our algorithm is O($q$), which has an exponential improvement over the classical segmentation algorithm, and the complexity will not increase as the image's size increases. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.