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Quantum image median filtering in the spatial domain

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Abstract

Spatial filtering is one principal tool used in image processing for a broad spectrum of applications. Median filtering has become a prominent representation of spatial filtering because its performance in noise reduction is excellent. Although filtering of quantum images in the frequency domain has been described in the literature, and there is a one-to-one correspondence between linear spatial filters and filters in the frequency domain, median filtering is a nonlinear process that cannot be achieved in the frequency domain. We therefore investigated the spatial filtering of quantum image, focusing on the design method of the quantum median filter and applications in image de-noising. To this end, first, we presented the quantum circuits for three basic modules (i.e., Cycle Shift, Comparator, and Swap), and then, we design two composite modules (i.e., Sort and Median Calculation). We next constructed a complete quantum circuit that implements the median filtering task and present the results of several simulation experiments on some grayscale images with different noise patterns. Although experimental results show that the proposed scheme has almost the same noise suppression capacity as its classical counterpart, the complexity analysis shows that the proposed scheme can reduce the computational complexity of the classical median filter from the exponential function of image size n to the second-order polynomial function of image size n, so that the classical method can be speeded up.

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(figure adapted from [31])

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References

  1. Glenn, B., Lomont, C., Cohen, C.: Quantum image processing (QuIP). In: Proceedings of the 32nd IEEE Conference on Applied Imagery and Pattern Recognition, Bellingham, WA, USA, pp. 39-44 (2003)

  2. Yan, F., Iliyasu, A.M.Le, Le, P.Q.: Quantum image processing: a review of advances in its security technologies. Int. J. Quantum Inf. 15(3), 1730001-(1–18) (2017)

    Article  MathSciNet  MATH  Google Scholar 

  3. Venegas-Andraca, S., Bose, S.: Storing, processing, and retrieving an image using quantum mechanics. In: Proceedings of SPIE Conference of Quantum Information and Computation, vol. 5105, pp. 134–147 (2003)

  4. Latorre, J.: Image Compression and Entanglement. arXiv:quant-ph/0510031 (2005)

  5. 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(1), 63–84 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Le, P., Iliyasu, A., Dong, F., Hirota, K.: A flexible representation and invertible transformations for images on quantum computers. N. Adv. Intell. Signal Process. Stud Comput. Intell. 372, 179–202 (2011)

    Article  Google Scholar 

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

    Article  ADS  MathSciNet  MATH  Google Scholar 

  8. Yuan, S., Mao, X., Xue, Y., Chen, L., Xiong, Q., Compare, A.: SQR: a simple quantum representation of infrared images. Quantum Inf. Process. 13(6), 1353–1379 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  9. Sun, B., Iliyasu, A., Yan, F., Dong, F., Hirota, K.: An RGB multi-channel representation for images on quantum computers. J. Adv. Comput. Intell. Intell. Inform. 17(3), 404–417 (2013)

    Article  Google Scholar 

  10. Sun, B., Le, P., Iliyasu, A., Yan, F., Garcia, J., Dong, F., Hirota, K.: Amulti-channel representation for images on quantum computers using the RGB color space. In: IEEE 7th International Symposium on Intelligent Signal Processing (WISP), pp. 1–6 (2011)

  11. Caraiman, S., Manta, V.I.: Image segmentation on a quantum computer. Quantum Inf. Process. 14(5), 1693–1715 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  12. Zhang, Y., Lu, K., Gao, Y., et al.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  13. Jiang, N., Dang, Y., Wang, J.: Quantum image matching. Quantum Inf. Process. 15(9), 3543–3572 (2016)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  14. Jiang, N., Dang, Y., Zhao, N.: Quantum image location. Int. J. Theor. Phys. 55(10), 4501–4512 (2016)

    Article  MATH  Google Scholar 

  15. Le, P.Q., Iliyasuy, A.M., Dong, F., et al.: Fast geometric transformations on quantum images. IAENG Int. J. Appl. Math. 40(3), 113–123 (2010)

    MathSciNet  Google Scholar 

  16. Jiang, N., Wu, W.Y., Wang, L., et al.: Quantum image pseudo color coding based on the density-stratified method. Quantum Inf. Process. 14(5), 1735–1755 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  17. Zhang, Y., Lu, K., Xu, K., et al.: Local feature point extraction for quantum images. Quantum Inf. Process. 14(5), 1573–1588 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  18. Simona, C., Vasile, I.M.: Image segmentation on a quantum computer. Quantum Inf. Process. 14(5), 1693–1715 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhou, R.G., Sun, Y.J., Fan, P.: Quantum image Gray-code and bit-plane scrambling. Quantum Inf. Process. 14(5), 1717–1734 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  20. Jiang, N., Wu, W.Y., Wang, J.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13(5), 1223–1236 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  21. Zhou, R.G., Wu, Q., Zhang, M.Q., et al.: Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int. J. Theor. Phys. 52(6), 1802–1817 (2013)

    Article  MathSciNet  Google Scholar 

  22. Jiang, N., Zhao, N., Wang, L.: LSB based quantum image steganography algorithm. Int. J. Theor. Phys. 55(1), 107–123 (2016)

    Article  MATH  Google Scholar 

  23. Iliyasu, A.M., Le, P.Q., Dong, F., et al.: Watermarking and authentication of quantum image based on restricted geometric transformations. Inf. Sci. 186(1), 126–149 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  24. Yan, F., Iliyasu, A.M., Sun, B., et al.: A duple watermarking strategy for multi-channel quantum images. Quantum Inf. Process. 14(5), 1675–1692 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  25. Iliyasu, A.M., Le, P.Q., Dong, F.Y., et al.: A framework for representing and producing movies on quantum computers. Int. J. Quantum Inf. 9(6), 1459–1497 (2011)

    Article  MATH  Google Scholar 

  26. Chris, L.: Quantum convolution and quantum correlation algorithms are physically impossible. arXiv:quant-ph/0309070, pp. 1–10 (2003)

  27. Simona, C., Vasile, I.M.: Quantum image filtering in the frequency domain. Adv. Electric. Comput. E. 13(3), 77–84 (2013)

    Article  Google Scholar 

  28. Yuan, S.Z., Mao, X.F., Zhou, J., et al.: Quantum image filtering in the spatial domain. Int. J. Theor. Phys. 56(8), 2495–2511 (2017)

    Article  MATH  Google Scholar 

  29. Yuan, S.Z., Lu, Y.L., Mao, X.F., et al.: Improved quantum image filtering in the spatial domain. https://doi.org/10.1007/s10773-017-3614-1

  30. Gonzalez, R.C., Woods, R.E.: Digital image processing, 3rd edn, pp. 178–179. Pearson Education, Inc., London (2010)

    Google Scholar 

  31. Wang, D., Liu, Z., Zhu, W., et al.: Design of quantum comparator based on extended general Toffoli gates with multiple targets. Comput. Sci. 39(9), 302–306 (2012)

    Google Scholar 

  32. Barenco, A., Bennett, C.H., Cleve, R., et al.: Elementary gates for quantum computation. Phys. Rev. A. 52(5), 3457–3467 (1995)

    Article  ADS  Google Scholar 

  33. Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14(5), 1589–1604 (2015)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  34. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information, pp. 22–24. Cambridge University Press, Cambridge (2000)

  35. Gonzalez, R.C. Woods, R.E., Eddins, S.L.: Image Processing Place. http://www.prenhall.com/ gonzalezwoods

  36. Iliyasu, A.M., Abuhasel, K.A., Yan, F.: A quantum-based image fidelity metric. In: Science and Information Conference, pp. 664–671 (2015)

  37. Iliyasu, A.M., Yan, F., Kaoru, H.: Metric for estimating congruity between quantum images. Entropy 18(10), 360–380 (2016)

    Article  ADS  Google Scholar 

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Acknowledgements

The authors appreciate the kind comments and constructive suggestions of three anonymous reviewers. This work was supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. F2015021). We thank Richard Haase, Ph.D, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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Correspondence to Panchi Li.

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Li, P., Liu, X. & Xiao, H. Quantum image median filtering in the spatial domain. Quantum Inf Process 17, 49 (2018). https://doi.org/10.1007/s11128-018-1826-9

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  • DOI: https://doi.org/10.1007/s11128-018-1826-9

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