Asymmetric scaling scheme over the two dimensions of a quantum image
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Asymmetric scaling scheme over the two dimensions of a quantum image Ri-Gui Zhou1,2 · Yu Cheng3
· Xiaofang Qi3 · Han Yu1,2 · Nan Jiang4
Received: 18 May 2020 / Accepted: 19 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Although quantum image scaling algorithms have been widely studied in recent years, almost all of them require the quantum image to be enlarged or reduced simultaneously in the horizontal and vertical directions. However, the scaling schemes that enlarge the quantum image in one direction and shrink it in the other direction are rarely involved. In this paper, a quantum image scaling scheme based on the extension of the bilinear interpolation method is proposed to achieve asymmetric scaling over the two dimensions of a quantum image for the first time. Firstly, the improved novel-enhanced quantum representation of digital images (INEQR) is employed to represent a 2n 1 ×2n 2 quantum image, and the bilinear interpolation is improved to use two adjacent pixels in the original image for interpolation. Then, the concrete circuits for the asymmetric scaling of quantum images are designed. Finally, the simulation results are given, and the complexity of the quantum circuits and the peak signal-to-noise ratio (PSNR) are used to quantitatively compare with the similar scheme proposed in another paper. The results show that the proposed scheme has lower computational complexity and better scaling effect than another scheme. Keywords Quantum image processing · Quantum image scaling · Bilinear interpolation · Quantum Fourier transform
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Yu Cheng [email protected] Xiaofang Qi [email protected]
1
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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Research Center of Intelligent Information Processing and Quantum Intelligent Computing, Shanghai 201306, China
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School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
4
College of Computer, Beijing University of Technology, Beijing 100124, China 0123456789().: V,-vol
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R.-G. Zhou et al.
1 Introduction At present, quantum image processing (QImP) is one of the most active fields in quantum computation and quantum information processing. Its concept was first proposed by Russian scholar Vlasov in 1997 [1], which did not attract much attention at that time. Until 2003, Beach [2] and Venegas-Andraca [3, 4], respectively, gave their own quantum image processing algorithms and tried to apply the existing quantum algorithms to the image, such as Grover’s database searching algorithms [5]. Soon, QImP began to catch the attention of researchers. And since 2010, the research on QImP has been booming. There are two main research directions in the field of QImP, including quantum image representation and quantum image processing algorithm. Quantum image representation aims to give an image representation model and store a digital image in quantum computers. So far, many quantum image representation models have been proposed, suc
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