Skip to main content
Log in

Construction of dual wavelet frame pairs and signal recovery

  • Published:
SeMA Journal Aims and scope Submit manuscript

Abstract

Signal processing is an enabling technology that helps us to denote any operation which modifies or analyzes the information contained in a signal. In this paper, we first decompose the original signal by a wavelet packet frame and analyze the coefficients. Then, by using dual wavelet frames, we reconstruct the original signal. In this reconstruction, the standard choice for duals which plays a key role is the canonical dual. Our aim is to develop new duals to obtain more accurate results. To this end, we consider wavelet frames which Fourier transform of generators form a partition of unity. Then we introduce several explicit duals for them and compare the advantage of these duals in signal processing. This indicates that we may obtain more reliable estimates by alternate duals.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Allabakash, S., Yasodha, P., Venkatramana Reddy, S., Srinivasulu, P.: Wavelet transform-based methods for removal of ground clutter and denoising the radar wind profiler data. Signal Process. IET 9, 440–448 (2015)

    Article  Google Scholar 

  2. Arefijamaal, A., Zekaee, E.: Signal processing by alternate dual Gabor frames. Appl. Comput. Harmon. Anal. 35, 535–540 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  3. Arefijamaal, A., Zekaee, E.: Image processing by alternate dual Gabor frames. Bull. Iran. Math. Soc. 42(6), 1305–1314 (2016)

    MathSciNet  MATH  Google Scholar 

  4. Casazza, P.G., Kutyniok, G., Lammers, M.C.: Duality principles in frame theory. Fourier Anal. Appl. 10(4), 383–408 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, Y., Zhang, Y., Hu, H., Ling, H.: A novel gray image watermarking scheme. J. Softw. 6(5), 849–856 (2011)

    Google Scholar 

  6. Christensen, O.: Frames and Bases. An Introductory Course. Birkhäuser, Boston (2008)

    MATH  Google Scholar 

  7. Christensen, O.: Pairs of dual Gabor frames with compact support and desired frequency localization. Appl. Comput. Harmon. Anal. 20, 403–410 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Christensen, O.: Time-frequency analysis and its applications in denoising. Department of informatics, University of Bergan, Thesis (2002)

  9. Christensen, O., Kim, R.Y.: On dual Gabor frame pairs generated by polynomials. Fourier Anal. Appl. 16, 11–16 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chui, C.K., Shi, X.: Orthonormal wavelets and tight frames with arbitrary real dilations. Appl. Comput. Harmon. Anal. 9(3), 243–264 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  11. Daubechies, I.: The wavelet transform, time frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961–1005 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  12. Daubechies, I., Grossmann, A., Meyer, Y.: Painless nonorthogonal expansions. J. Math. Phys. 27, 1271–1283 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  13. Deng, L., Yu, C.L., Chakrabarti, C., Kim, J., Narayanan, V.: Efficient image reconstruction using partial 2D Fourier transform. In: Proceedings of the IEEE Workshop on Signal Processing Systems, pp. 49–54. SiPS, Washington, DC Metro Area, USA (2008)

  14. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  15. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Am. Stat. Assoc. 90(432), 1200–1224 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  16. Feichtinger, H.G., Strohmer, T.: Gabor Analysis and Algorithms: Theory and Applications. Birkhäuser, Boston (1997)

    MATH  Google Scholar 

  17. Gautier, M., Lienard, J.: Efficient wavelet packet modulation for wireless communication. In: Proceedings of the 3rd Advanced International Conference, pp. 1–19. France Telecom R and D, France (2007)

  18. Găianu, M., Onchis, D.M.: Face and marker detection using Gabor frames on GPUs. Signal Process. 96, 90–93 (2014)

    Article  Google Scholar 

  19. He, J., Li, Z., Qian, H.: Cryptography based on spatiotemporal chaos system and multiple maps. J. Softw. 5(4), 421–428 (2010)

    Article  Google Scholar 

  20. Heil, C.E., Walnut, D.F.: Continuous and discrete wavelet transforms. SIAM Rev. 31, 628–666 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  21. Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projection, 2nd edn. Springer, New York (2009)

    Book  MATH  Google Scholar 

  22. Huang, F., Qu, X.: Design of image encryption algorithm based on compound two-dimensional maps. J. Softw. 6(10), 1953–1960 (2011)

    Google Scholar 

  23. Janssen, A.J.E.M.: Duality and biorthogonality for Weyl–Heisenberg frames. Fourier Anal. Appl. 1(4), 403–436 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  24. Lemvig, J.: Constructing pairs of dual bandlimited framelets with desired time localization. Adv. Comput. Math. 30, 231–248 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  25. Mallat, S.G.: A wavelet tour of signal processing. Academic Press, San Diego (1999)

    MATH  Google Scholar 

  26. Porter, R., Canagarajah, N.: Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes. IEE Proc. Vis. Image Signal Process. 144(3), 180–188 (1997)

    Article  Google Scholar 

  27. Ron, A., Shen, Z.: Weyl–Heisenberg frames and Riesz bases in \(L^2(\mathbb{R}^d )\). Duke Math. J. 89, 237–282 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  28. Tong, C.S., Leung, K.T.: Super-resolution reconstruction based on linear interpolation of wavelet coefficients. Multidimens. Syst. Signal Process. 18(2–3), 153–171 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  29. Thamarai, P., Adalarasu, K.: Denoising of EEG, ECG and PPG signals using wavelet transform. J. Pharm. Sci. and Res. 10(1), 156–161 (2018)

    Google Scholar 

  30. Verma, R., Mahrishi, R., Srivastava, G. K., Siddavatam, R.: A novel image reconstruction using second generation wavelets. In: IEEE International Conference on Advances in Recent Technologies in Communication and Computing, pp. 509–513. IEEE Press, Kerala (2009)

  31. Wexler, J., Raz, S.: Discrete Gabor expansions. Signal Process. 21, 207–221 (1990)

    Article  Google Scholar 

  32. Yang, X., Min, J., Shi, Y.: Parametrisation construction frame of lifting scheme. IET Signal Process. 5, 1–15 (2011)

    Article  MathSciNet  Google Scholar 

  33. Zhang, Y., Yuanyuan, W., Chen, Z.: Efficient discrete cosine transform model-based algorithm for photoacoustic image reconstruction. J. Biomed. Opt. 18(6), 066008(1)–066008(9) (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Akbar Arefijamaal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arefijamaal, A.A., Arabyani Neyshaburi, F. & Matindoost, S. Construction of dual wavelet frame pairs and signal recovery. SeMA 76, 27–36 (2019). https://doi.org/10.1007/s40324-018-0156-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40324-018-0156-2

Keywords

Mathematics Subject Classification

Navigation