Abstract
Edge detection is one of the most important techniques in the field of image processing, which has a great influence on the subsequent research of feature extraction, description and target recognition. By analyzing the traditional Prewitt edge detection algorithm, the algorithm has been found some shortcomings, such as coarse edge detection and false edge detection caused by artificial selection of threshold. In this paper, quantum image edge extraction for the novel enhanced quantum representation (NEQR) is proposed based on improved Prewitt operator, which combines the non-maximum suppression method and adaptive threshold value method. The quantum image model of NEQR utilizes the superposition state of qubit sequence to store all the pixels of an image, which can calculate the gradients of the image intensity of all the pixels simultaneously. In addition, the non-maximal suppression can refine the edge, and the adaptive threshold can reduce the misjudgment of edge points. By analyzing the quantum circuit of realizing image edge extraction and the simulation results, compared with all the classical edge extraction algorithms and some existing quantum edge extraction algorithms, our proposed scheme can achieve a significant efficiency.




















Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Gonzalez, R.C., Wintz, P.: Digital image processing. Prentice Hall Int. 28, 484–486 (2008)
Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982)
Deutsch, D.: Quantum theory, the church-turing principle and the universal quantum computer. Proc. R. Soc. Lond. A. Math. Phys. Sci. 400, 97–117 (1985)
Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings on Annual Symppsium Foundation of Computer Science, pp. 124–134. IEEE Computer Society Press, Los Alamitos, CA (1994)
Grover, L.K.B.T.-T.A.S. on T. of C.: Fast quantum mechanical algorithm for database search. Presented at the (1996)
Fei, Y., Iliyasu, A.M., Le, P.Q.: Quantum image processing: a review of advances in its security technologies. Int. J. Quantum Inf. 15, 1730001 (2017)
Fei, Y., Iliyasu, A.M., Venegas-Andraca, S.E.: A survey of quantum image representations. Quantum Inf. Process. 15, 1–35 (2016)
Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceedings of SPIE Conference of Quantum Information and Computation (2003)
Latorre, J.I.: Image compression and entanglement (2005). arXiv:quant-ph/0510031
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)
Li, H.S., Zhu, Q., Zhou, R.G., Song, L., Yang, X.J.: Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state. Quantum Inf. Process. 13, 991–1011 (2014)
Li, H.S., Zhu, Q., Lan, S., Shen, C.Y., Zhou, R., Mo, J.: Image storage, retrieval, compression and segmentation in a quantum system. Quantum Inf. Process. 12, 2269–2290 (2013)
Sang, J., Shen, W., Li, Q.: A novel quantum representation of color digital images. Quantum Inf. Process. 16, 42 (2017)
Le, P.Q., Iliyasu, A.M., Dong, F., et al.: Strategies for designing geometric transformations on quantum images. Theor. Comput. Sci. 412, 1406–1418 (2011)
Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Fast geometric transformations on quantum images. Int. J. Appl. Math. 40, 113–123 (2010)
Jian, W., Nan, J., Luo, W.: Quantum image translation. Quantum Inf. Process. 14, 1589–1604 (2014)
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)
Nan, J., Luo, W.: 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. 7, 2511 (2017)
Zhou, R.G., Liu, X., Luo, J.: Quantum circuit realization of the bilinear interpolation method for GQIR. Int. J. Theor. Phys. 56, 2966–2980 (2017)
Heidari, S., Farzadnia, E.: A novel quantum LSB-based steganography method using the Gray code for colored quantum images. Quantum Inf. Process. 16, 1–28 (2017)
Zhang, W.W., Gao, F., Liu, B., Wen, Q.Y., Chen, H.: A watermark strategy for quantum images based on quantum Fourier transform. Quantum Inf. Process. 12, 793–803 (2013)
Zhou, R.G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16, 1–21 (2017)
Hu, W.W., Zhou, R.G., Luo, J., Liu, B.Y.: LSBs-based quantum color images watermarking algorithm in edge region. Quantum Inf. Process. 18, 16 (2019)
Zhang, Y., Lu, K., Gao, Y.H.: QSobel: a novel quantum image edge extraction algorithm. Sci. China Inf. Sci. 58, 1–13 (2014)
Fan, P., Zhou, R.G., Hu, W.W., Jing, N.H.: Quantum image edge extraction based on Laplacian operator and zero-cross method. Quantum Inf. Process. 18, 27 (2019)
Fan, P., Zhou, R.G., Hu, W., Jing, N.: Quantum image edge extraction based on classical Sobel operator for NEQR. Quantum Inf. Process. 18, 24 (2019)
Yuan, S., Mao, X., Li, T., Xue, Y., Chen, L., Xiong, Q.: Quantum morphology operations based on quantum representation model. Quantum Inf. Process. 14, 1625–1645 (2015)
Zhou, R.G., Fan, P., Tan, C., Hu, W.: Quantum gray-scale image dilation/erosion algorithm based on quantum loading scheme. J. Comput. 29, 220–227 (2018)
Zhou, R.G., Chang, Z.B., Fan, P., Li, W., Huan, T.: Tian: quantum image morphology processing based on quantum set operation. Int. J. Theor. Phys. 54, 1974–1986 (2015)
Jiang, N., Dang, Y., Wang, J.: Quantum image matching. Quantum Inf. Process. 15, 3543–3572 (2016)
Duan, R.L., Qing-Xiang, L.I., Yu-He, L.I.: Summary of image edge detection. Opt. Tech. 3, 415–419 (2005)
Sobel, I.: Camera Models and Machine Perception. Dissertation, Stanford University (1970)
Prewitt, J.M.S.: Object enhancement and extraction. Pict. Process. Psychopictorics. 10, 15–19 (1970)
Kirsch, R.A.: Computer determination of the constituent structure of biological images. Comput. Biomed. Res. 4, 315–328 (1971)
Canny, J.: A Computational Approach To Edge Detection. IEEE TPAML (1986)
Islam, M.S., Rahman, M.M., Begum, Z., Hafiz, M.Z.: Low cost quantum realization of reversible multiplier circuit. Inf. Technol. J. 8, 208–213 (2009)
Thapliyal, H., Ranganathan, N.: Design of efficient reversible binary subtractors based on a new reversible gate (2009)
Thapliyal, H., Ranganathan, N.: A new design of the reversible subtractor circuit. In: 2011 11th IEEE International Conference on Nanotechnology. IEEE (2011)
Khosropour, A., Aghababa, H., Forouzandeh, B.: Quantum division circuit based on restoring division algorithm. In: 2011 Eighth International Conference on Information Technology: New Generations. IEEE (2011)
Wang, D., Liu, Z.H., Zhu, W.N., Li, S.Z.: Design of quantum comparator based on extended general Toffoli gates with multiple targets. Comput. Sci. 39, 302–306 (2012)
Barenco, A., Bennett, C.H., Cleve, R., Divincenzo, D.P., Weinfurter, H., et al.: Elementary gates for quantum computation. Phys. Rev. A 52, 3457 (1995)
Nielsen, M.A., Chuang, I.: 1: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Tseng, C.C., Hwang, T.M.: Quantum digital image processing algorithms (2003)
Fu, X., Ding, M., Sun, Y., Chen, S.: A new quantum edge detection algorithm for medical images. Proc. SPIE Int. Soc. Opt. Eng. 7497, 749724–749727 (2009)
Lin, H., Zhao, C.S., Shu, N.: Edge detection based on Canny operator and evaluation. J. Heilongjiang Inst. Technol. 2, 3–6 (2003)
Acknowledgements
This work is supported by the National Key R&D Plan under Grant No. 2018YFC1200200, National Natural Science Foundation of China under Grant No. 61463016 and “Science and technology innovation action plan” of Shanghai in 2017 under Grant No. 17510740300.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, RG., Yu, H., Cheng, Y. et al. Quantum image edge extraction based on improved Prewitt operator. Quantum Inf Process 18, 261 (2019). https://doi.org/10.1007/s11128-019-2376-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11128-019-2376-5