Skip to main content
SpringerLink
Log in

Quantum image edge extraction based on improved Prewitt operator

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Gonzalez, R.C., Wintz, P.: Digital image processing. Prentice Hall Int. 28, 484–486 (2008)

    MATH  Google Scholar 

  2. Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982)

    Article  MathSciNet  Google Scholar 

  3. 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  4. 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)

  5. Grover, L.K.B.T.-T.A.S. on T. of C.: Fast quantum mechanical algorithm for database search. Presented at the (1996)

  6. 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)

    Article  MathSciNet  MATH  Google Scholar 

  7. Fei, Y., Iliyasu, A.M., Venegas-Andraca, S.E.: A survey of quantum image representations. Quantum Inf. Process. 15, 1–35 (2016)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  8. 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)

  9. Latorre, J.I.: Image compression and entanglement (2005). arXiv:quant-ph/0510031

  10. Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9, 1–11 (2010)

    Article  MathSciNet  Google Scholar 

  11. 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)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12, 2833–2860 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  13. 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  14. 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  15. Sang, J., Shen, W., Li, Q.: A novel quantum representation of color digital images. Quantum Inf. Process. 16, 42 (2017)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  16. 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)

    Article  MathSciNet  MATH  Google Scholar 

  17. Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Fast geometric transformations on quantum images. Int. J. Appl. Math. 40, 113–123 (2010)

    MathSciNet  MATH  Google Scholar 

  18. Jian, W., Nan, J., Luo, W.: Quantum image translation. Quantum Inf. Process. 14, 1589–1604 (2014)

    MathSciNet  MATH  Google Scholar 

  19. 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)

    Article  MathSciNet  MATH  Google Scholar 

  20. Nan, J., Luo, W.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14, 1559–1571 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhou, R.G., Hu, W., Fan, P., Ian, H.: Quantum realization of the bilinear interpolation method for NEQR. Sci. Rep. 7, 2511 (2017)

    Article  ADS  Google Scholar 

  22. 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)

    Article  MathSciNet  MATH  Google Scholar 

  23. 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)

    Article  MathSciNet  MATH  Google Scholar 

  24. 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  25. Zhou, R.G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16, 1–21 (2017)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  26. 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)

    Article  ADS  MATH  Google Scholar 

  27. Zhang, Y., Lu, K., Gao, Y.H.: QSobel: a novel quantum image edge extraction algorithm. Sci. China Inf. Sci. 58, 1–13 (2014)

    MATH  Google Scholar 

  28. 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)

    Article  ADS  MATH  Google Scholar 

  29. 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)

    Article  ADS  MATH  Google Scholar 

  30. 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)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  31. 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)

    Google Scholar 

  32. 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)

    Article  MATH  Google Scholar 

  33. Jiang, N., Dang, Y., Wang, J.: Quantum image matching. Quantum Inf. Process. 15, 3543–3572 (2016)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  34. Duan, R.L., Qing-Xiang, L.I., Yu-He, L.I.: Summary of image edge detection. Opt. Tech. 3, 415–419 (2005)

    Google Scholar 

  35. Sobel, I.: Camera Models and Machine Perception. Dissertation, Stanford University (1970)

  36. Prewitt, J.M.S.: Object enhancement and extraction. Pict. Process. Psychopictorics. 10, 15–19 (1970)

    Google Scholar 

  37. Kirsch, R.A.: Computer determination of the constituent structure of biological images. Comput. Biomed. Res. 4, 315–328 (1971)

    Article  Google Scholar 

  38. Canny, J.: A Computational Approach To Edge Detection. IEEE TPAML (1986)

  39. 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)

    Article  Google Scholar 

  40. Thapliyal, H., Ranganathan, N.: Design of efficient reversible binary subtractors based on a new reversible gate (2009)

  41. Thapliyal, H., Ranganathan, N.: A new design of the reversible subtractor circuit. In: 2011 11th IEEE International Conference on Nanotechnology. IEEE (2011)

  42. 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)

  43. 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)

    Google Scholar 

  44. 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)

    Article  ADS  Google Scholar 

  45. Nielsen, M.A., Chuang, I.: 1: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  46. Tseng, C.C., Hwang, T.M.: Quantum digital image processing algorithms (2003)

  47. 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)

    Google Scholar 

  48. Lin, H., Zhao, C.S., Shu, N.: Edge detection based on Canny operator and evaluation. J. Heilongjiang Inst. Technol. 2, 3–6 (2003)

    Google Scholar 

Download references

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

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Ri-Gui Zhou, Han Yu, Yu Cheng & Feng-Xin Li

  2. Research Center of Intelligent Information Processing and Quantum Intelligent Computing, Shanghai, 201306, China

    Ri-Gui Zhou, Han Yu, Yu Cheng & Feng-Xin Li

Authors
  1. Ri-Gui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng-Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Han Yu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-019-2376-5

Keywords

Search

Navigation