The dual-threshold quantum image segmentation algorithm and its simulation

• DOI: 10.1007/s11128-020-02932-x
• Publication Date: 2020-11-23T03:02:57Z
• Authors: Suzhen Yuan; Chao Wen; Bo Hang; Yu Gong
• Keywords: Quantum sort

Various quantum computing simulation platforms have developed rapidly in the last 3 years. However, few quantum image processing algorithms are simulated in these platforms. In this paper, we design a dual-threshold quantum image segmentation algorithm and simulate it in IBM Q Experience platform through Qiskit extension. The NEQR quantum image representation model is firstly optimized and simulated, which is found that the number of the auxiliary qubits will not increase as the image’s size increases. Then, an efficient quantum comparator to realize the comparison of two numbers is designed. And finally, the high parallelism image segmentation algorithm is proposed and simulated. Suppose the size of an image is $${{2}^{n}}\times {{2}^{n}}$$ and the gray-scale scope is [0, $${{2}^{q}}-1$$ ], the time complexity analysis for the quantum image segmentation algorithm shows that the number of basic quantum gate required is proportional to q and will not increase as image’s size increases. Thus, the proposed quantum segmentation algorithm is highly parallelism and has polynomial time complexity. In addition, the simulation part of this paper will provide reference for other quantum image processing algorithms.