Abstract
Quantum image processing has recently emerged as an essential problem in practical tasks, e.g. real-time image matching. Previous studies have shown that the superposition and entanglement of quantum can greatly improve the efficiency of complex image processing. In this paper, a fuzzy quantum image matching scheme based on gray-scale difference is proposed to find out the target region in a reference image, which is very similar to the template image. Firstly, we employ the proposed enhanced quantum representation (NEQR) to store digital images. Then some certain quantum operations are used to evaluate the gray-scale difference between two quantum images by thresholding. If all of the obtained gray-scale differences are not greater than the threshold value, it indicates a successful fuzzy matching of quantum images. Theoretical analysis and experiments show that the proposed scheme performs fuzzy matching at a low cost and also enables exponentially significant speedup via quantum parallel computation.
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References
Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982)
Shor, P.W.: Polynomial time algorithms for discrete logarithms and factoring on a quantum computer. In: Proceedings of the First International Symposium on Algorithmic Number Theory, ANTS-I, pp. 289 (1994)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)
Venegas-Addraca, S.E.: Storing, processing, and retrieving an image using quantum mechanics. In: Proceedings of SPIE-The international Society for Optical Engineering, pp. 1085–1090 (2003)
Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9, 1–11 (2010)
Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10, 63–84 (2011)
Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a Novel enhanced quantum representation of digital images. Quantum Inf. Process. 12, 2833–2860 (2013)
Zhou, R.G., Tan, C., Ian, H.: Global and local translation designs of quantum image based on FRQI. Int. J. Theor. Phys. 56, 1382–1398 (2017)
Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14, 1589–1604 (2015)
Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14, 1559–1571 (2015)
Zhou, R. -G., Hu, W., Fan, P., Ian, H.: Quantum realization of the bilinear interpolation method for NEQR. Sci. Rep. p. 7 (2017)
Iliyasu, A.M., Le, P.Q., Dong, F., Hirota, K.: Watermarking and authentication of quantum images based on restricted geometric transformations. Inf. Sci. 186, 126–149 (2012)
Miyake, S., Nakamae, K.: A quantum watermarking scheme using simple and small-scale quantum circuits. Quantum Inf. Process. 15, 1849–1864 (2016)
Zhang, W.W., Gao, F., Liu, B., Jia, H.Y., Wen, Q.Y., Chen, H.: A quantum watermark protocol. Int. J. Theor. Phys. 52, 504–513 (2013)
Heidari, S., Naseri, M.: A novel LSB based quantum watermarking. Int. J. Theor. Phys. 55, 4205–4218 (2016)
Zhang, Y., Lu, K., Gao, Y.H.: QSObel: a novel quantum image edge extraction algorithm. Sci. China Inf. Sci. 58, 1–13 (2014)
Zhang, Y., Lu, K., Xu, K., Gao, Y., Wilson, R.: Local feature point extraction for quantum images. Quantum Inf. Process. 14, 1573–1588 (2015)
Curtis, D., Meyer, D.A.: Towards quantum template matching. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 134 (2004)
Yang, Y.G., Zhao, Q.Q., Sun, S.J.: Novel quantum gray-scale image matching. Optik 126, 3340–3343 (2015)
Jiang, N., Dang, Y., Wang, J.: Quantum image matching. Quantum Inf. Process. 15, 3543–3572 (2016)
Dang, Y., Jiang, N., Hu, H., Zhang, W.: Analysis and improvement of the quantum image matching. Quantum Inf. Process. 16, 1–13 (2017)
Thapliyal, H., Ranganathan, N.: Design of efficient reversible binary subtractors based on a new reversible gate. In: Proceedings of the 2009 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2009, pp. 229–234 (2009)
Thapliyal, H., Ranganathan, N.: A new design of the reversible subtractor circuit. In: Proceedings of the IEEE Conference on Nanotechnology, pp. 1430–1435 (2011)
Zhou, R.G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16, 1–21 (2017)
Jiang, N., Wang, J., Mu, Y.: Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio. Quantum Inf. Process. 14, 4001–4026 (2015)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No. 61463016, 61763014, “Science and technology innovation action plan” of Shanghai in 2017 under Grant No. 17510740300, the advantages of scientific and technological innovation team of Nanchang City under Grant No. 2015CXTD003, and the Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province.
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Luo, G., Zhou, RG., Liu, X. et al. Fuzzy Matching Based on Gray-scale Difference for Quantum Images. Int J Theor Phys 57, 2447–2460 (2018). https://doi.org/10.1007/s10773-018-3766-7
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DOI: https://doi.org/10.1007/s10773-018-3766-7