The dual-threshold quantum image segmentation algorithm and its simulation
- PDF / 1,981,385 Bytes
- 21 Pages / 439.37 x 666.142 pts Page_size
- 4 Downloads / 285 Views
The dual-threshold quantum image segmentation algorithm and its simulation Suzhen Yuan1 · Chao Wen1 · Bo Hang 2 · Yu Gong3 Received: 28 December 2019 / Accepted: 4 November 2020 / Published online: 23 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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 2n × 2n and the gray-scale scope is [0, 2q − 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. Keywords Quantum image processing · Quantum image representation · Quantum image segmentation · Quantum parallelism · Quantum comparator
B B
Bo Hang [email protected] Yu Gong [email protected] Suzhen Yuan [email protected] Chao Wen [email protected]
1
The College of Opto Electronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
Computer School, Hubei University of Arts and Science, Xiangyang 441054, China
3
College of Aerospace Engineering, Chongqing University, Chongqing 400044, China
123
425
Page 2 of 21
S. Yuan et al.
1 Introduction Quantum image processing is an interdisciplinary subject of quantum computing and image processing. The task of quantum image processing is to design the representation of image in quantum computer and quantum image processing algorithms based on these representations. In the last 20 years, quantum image processing have developed very fast. In the review literature [1], many quantum image representations have been introduced, including three representative representations [2–4]. They are the qubit lattice representation [2], a flexible representation of quantum image (FRQI) [3], and the novel enhanced quantum representation of digital image (NEQR) model [4]. In qubit lattice representation, the color information is encoded in the probability amplitudes of one qubit state, but the position information is not encoded specifically by qubits; thus, it is hard to control the position information in image processing task. FRQI model is a normalized superposition
Data Loading...
