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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2013 - 13


Quantum Image Filtering in the Frequency Domain

CARAIMAN, S. See more information about CARAIMAN, S. on SCOPUS See more information about CARAIMAN, S. on IEEExplore See more information about CARAIMAN, S. on Web of Science, MANTA, V. I. See more information about MANTA, V. I. on SCOPUS See more information about MANTA, V. I. on SCOPUS See more information about MANTA, V. I. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (708 KB) | Citation | Downloads: 767 | Views: 4,495

Author keywords
quantum image processing, quantum Fourier transform, quantum oracle, image filtering

References keywords
quantum(28), images(7), computation(6), processing(5), image(5), hirota(5), dong(5), quant(4), iliyasu(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 77 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03013
Web of Science Accession Number: 000326321600013
SCOPUS ID: 84884914407

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In this paper we address the emerging field of Quantum Image Processing. We investigate the use of quantum computing systems to represent and manipulate images. In particular, we consider the basic task of image filtering. We prove that a quantum version for this operation can be achieved, even though the quantum convolution of two sequences is physically impossible. In our approach we use the principle of the quantum oracle to implement the filter function. We provide the quantum circuit that implements the filtering task and present the results of several simulation experiments on grayscale images. There are important differences between the classical and the quantum implementations for image filtering. We analyze these differences and show that the major advantage of the quantum approach lies in the exploitation of the efficient implementation of the quantum Fourier transform.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 350] [SCOPUS Times Cited 406]

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[CrossRef] [Web of Science Times Cited 1183]

[3] E. Bernstein and U. Vazirani, "Quantum Complexity Theory", SIAM J. Comput. 26, 5 pp. 1411-1473, 1997,
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[4] P. Shor, "Algorithms For Quantum Computation: Discrete Logarithms and Factoring", in: SFCS '94: Proc. of the 35th Annual Symposium on Foundations of Computer Science, IEEE Computer Society, 1994, pp. 124-134,

[5] A. Fijany, C. Williams, "Quantum Wavelet Transform: Fast Algorithm and Complete Circuits", arXiv:quant-ph/9809004, 1998.

[6] A. Klappenecker, M. Roetteler, "Discrete Cosine Transforms on Quantum Computers", arXiv:quant-ph/0111038, 2001.

[7] C. C. Tseng, T. M. Hwang, "Quantum Circuit Design of 8x8 Discrete Cosine Transform Using Its Fast Computation Flow Graph", in: Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on, pp. 828-831 Vol. 1,

[8] S. Venegas-Andraca and S. Bose, "Storing, Processing and Retrieving an Image Using Quantum Mechanics," in Proc. of the SPIE Conf. Quantum Information and Computation, 2003, pp. 137-147,
[CrossRef] [Web of Science Times Cited 185] [SCOPUS Times Cited 221]

[9] P. Le, F. Dong, and K. Hirota, "A Flexible Representation of Quantum Images for Polynomial Preparation, Image Compression, and Processing Operations," Quantum Inf. Process., vol. 10, pp. 63-84, 2011,
[CrossRef] [Web of Science Times Cited 282] [SCOPUS Times Cited 317]

[10] P. Q. Le, A. M. Iliyasu, F. Dong, K. Hirota, "Strategies for Designing Geometric Transformations on Quantum Images", Theor. Comput. Sci. 412 (2011), 1406-1418,
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 87]

[11] P. Q. Le, A. M. Iliyasu, F. Dong, K. Hirota, "Efficient Color Transformations on Quantum Images", JACIII 15 (2011) 698-706.

[12] F. Yan, P. Q. Le, A. M. Iliyasu, B. Sun, J. A. Garcia, F. Dong and K. Hirota, "Assessing the Similarity of Quantum Images Based on Probability Measurements," 2012 IEEE World Congress on Computational Intelligence, Brisbane, 10-15 June 2012, pp. 1-6,
[CrossRef] [SCOPUS Times Cited 30]

[13] A. M. Iliyasu, P. Q. Le, F. Dong, and K. Hirota, "Watermarking and authentication of quantum images based on restricted geometric transformations". Inf. Sci. 186, 1, pp. 126-149, 2012,
[CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 154]

[14] W. Zhang, F. Gao, B. Liu, Q. Wen, and H. Chen, "A watermark strategy for quantum images based on quantum Fourier transform" Quantum Inf. Process. 12, 2, pp. 793-803, 2013,
[CrossRef] [Web of Science Times Cited 106] [SCOPUS Times Cited 114]

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[16] L. K. Grover, "A Fast Quantum Mechanical Algorithm for Database Search", in: Proc. of the 28th annual ACM Symposium on Theory of Computing, STOC '96, ACM, New York, NY, USA, 1996, pp. 212-219,
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[17] S. Venegas-Andraca, J. Ball, "Processing Images in Entangled Quantum Systems", Quantum Inf. Process. 9 (2010) 1-11,
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[18] J. Latorre, "Image Compression and Entanglement", arXiv:quantph/0510031, 2005.

[19] C. Lomont, "Quantum Convolution and Quantum Correlation Algorithms Are Physically Impossible," arXiv:quant-ph/0309070, 2003.

[20] M. Nielsen and I. Chuang, "Quantum Computation and Quantum Information", Cambridge Series on Information and the Natural Sciences. Cambridge, UK: Cambridge University Press, 2000

[21] G. Brassard, P. Hoyer, M. Mosca, A. Tapp, "Quantum Amplitude Amplification and Estimation", arXiv:quant-ph/0005055, 2000.

[22] S. Caraiman and V. Manta, "Image Processing Using Quantum Computing", 16th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, 12-14 October 2012, pp. 1-6.

References Weight

Web of Science® Citations for all references: 3,033 TCR
SCOPUS® Citations for all references: 5,164 TCR

Web of Science® Average Citations per reference: 138 ACR
SCOPUS® Average Citations per reference: 235 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-04-15 22:43 in 93 seconds.

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