Skip to main content
Log in

Estimation of Optimal Parameter of Regularization of Signal Recovery

  • Published:
Radioelectronics and Communications Systems Aims and scope Submit manuscript

Abstract

In this paper there are researched regularizing properties of discretization in a space of output signals for some linear operator equation with noisy data. The essence of proposed method is selection of discretization level which is a parameter of the regularization in this context by the principle of equality of random and deterministic components of the input signal recovering error. It is shown the method, i.e. the solution which is discrete by input signal is stable to small inaccuracies in input signal. At that in case of definite level of output signal measurements inaccuracy the recovering error of input signal is unambiguously defined by input signal sampling increment that allows to select reasonably the regularization parameter for specific criterion, for example, for definite measurements inaccuracy. Specific calculations and examples are represented in explicit form for single-dimension case but this does not restricts generality of proposed method.

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.

Similar content being viewed by others

References

  1. A. N. Tikhonov, V. Ya. Arsenin. The Methods of Ill-Conditioned Problems Solution [in Russian] (Nauka, Moscow, 1979).

    Google Scholar 

  2. V. A. Morozov, Methods of Regularization of Unstable Problems [in Russian] (Izd-vo Moskovskogo Un-ta, Moscow, 1987).

    Google Scholar 

  3. A. B. Bakushinskiy, A. V. Goncharovskiy, Ill-Conditioned Problems. Numerical Methods and Applications [in Russian] (Izd-vo Moskovskogo Un-ta, Moscow, 1989).

    Google Scholar 

  4. M. Benning, M. Burger, “Modern regularization methods for inverse problems,” Acta Numerica 27, 1 (2018). DOI: 10.1017/S0962492918000016.

    Article  MathSciNet  Google Scholar 

  5. V. P. Tanana, A. I. Sidikova, Optimal Methods for Ill-Posed Problems. With Applications to Heat Conduction (De Gruyter, Berlin-Boston, 2018). ISBN: 978-3-11-057721-1.

    Book  MATH  Google Scholar 

  6. Ugayraj, K. Mulani, P. Talukdar, A. Das, R. Alagirusamy, “Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems,” Int. J. Heat Mass Transfer 89, 359 (2015). DOI: 10.1016/j.ijheatmasstransfer.2015.05.015.

    Article  Google Scholar 

  7. M. Stille, M. Kleine, J. Hägele, J. Barkhausen, T. M. Buzug, “Augmented likelihood image reconstruction,” IEEE Trans. Medical Imaging 35, No. 1, 158 (2016). DOI: 10.1109/TMI.2015.2459764.

    Article  Google Scholar 

  8. T. Gass, G. Székely, O. Goksel, “Consistency-based rectification of nonrigid registrations,” J. Medical Imaging 2, 014005 (2015). DOI: 10.1117/1.JMI.2.1.014005.

    Article  Google Scholar 

  9. S. K. Turitsyn, J. E. Prilepsky, S. T. Le, S. Wahls, L. L. Frumin, M. Kamalian, S. A. Derevyanko, “Nonlinear Fourier transform for optical data processing and transmission: advances and perspectives,” Optica 4, No. 3, 307 (2017). DOI: 10.1364/OPTICA.4.000307.

    Article  Google Scholar 

  10. J. Adler, O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Problems 33, No. 12, 124007 (2017). DOI: 10.1088/1361-6420/aa9581.

    Article  MathSciNet  MATH  Google Scholar 

  11. B. Kaltenbacher, “Regularization by projection with a posteriori discretization level choice for linear and nonlinear ill-posed problems,” Inverse Problems 16, No. 5, 1523 (2000). DOI: 10.1088/0266-5611/16/5/322.

    Article  MathSciNet  MATH  Google Scholar 

  12. B. Kaltenbacher, J. Offtermatt, “A convergence analysis of regularization by discretization in preimage space,” Math. Comp. 81, 2049 (2012). DOI: 10.1090/S0025-5718-2012-02596-8.

    Article  MathSciNet  MATH  Google Scholar 

  13. B. Kaltenbacher (Blaschke), H. W. Engl, W. Grever, M. Klibanov, “An application of Tikhonov regularization to phase retrieval,” Nonlinear World 3, 771 (1996).

    MathSciNet  MATH  Google Scholar 

  14. B. Kaltenbacher, “Boundary observability and stabilization for Westervelt type wave equations without interior damping,” Appl. Math. Optim. 62, No. 3, 381 (2010). DOI: 10.1007/s00245-010-9108-7.

    Article  MathSciNet  MATH  Google Scholar 

  15. D. V. Dovnar, K. G. Predko, “Method of eliminating rectilinear uniform blurring of an image,” Optoelectron. Instrument. Data Process., No. 6, 100 (1984).

    Google Scholar 

  16. D. V. Dovnar, K. G. Predko, “Use of orthogonalization of the mappings of basis functions for regularized restoration of a signal,” USSR Computational Mathematics and Mathematical Physics 26, 13 (1986). DOI: 10.1016/0041-5553(86)90070-4.

    Article  MATH  Google Scholar 

  17. Yu. E. Voskoboynikov, “Estimation of the optimal regularization parameter of an iterative wavelet algorithm for signal recovery,” Optoelectron. Instrument. Data Process. 49, No. 2, 115 (2013). DOI: 10.3103/S8756699013020027.

    Article  Google Scholar 

  18. Yu. E. Voskoboynikov, V. A. Litasov, “Stable algorithm for recover of image in case of ill-conditioned instrument function,” Avtometriya 42, No. 6, 3 (2006). URI: https://www.iae.nsk.su/images/stories/5_Autometria/5_Archives/2006/6/3-15.pdf.

    Google Scholar 

  19. S. Pereverzev, E. Schock, “On the adaptive selection of the parameter in regularization of ill-posed problems,” SIAM J. Numerical Analysis 43, No. 5, 2060 (2006). URI: https://www.jstor.org/stable/4101307.

    Article  MathSciNet  MATH  Google Scholar 

  20. M. Y. Mints, E. D. Prilepskii, “Image discretization method applied for extended object restoration,” Optika i Spectroskopiya 75, 696 (1993).

    Google Scholar 

  21. S. P. Luttrell, “A new method of sample optimization,” Optica Acta 32, No. 3, 255 (1985). DOI: 10.1080/713821739.

    Article  MathSciNet  Google Scholar 

  22. B. R. Frieden, “Image-restoration using a norm of maximum information,” Optical Engineering 19, No. 3, 290 (1980). DOI: 10.1117/12.7972512.

    Article  Google Scholar 

  23. K. Kido, Discrete Fourier Transform, in Digital Fourier Analysis: Fundamentals. Undergraduate Lecture Notes in Physics (Springer, New York, 2015). DOI: 10.1007/978-1-4614-9260-3_4.

    Google Scholar 

  24. M. Born, E. Volf, Basic Principles of Optic [in Russian] (Nauka, Moscow, 1973).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Evgeni D. Prilepsky.

Additional information

Original Russian Text © E.D. Prilepsky, J.E. Prilepsky, 2018, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2018, Vol. 61, No. 9, pp. 522–535.

The authors are grateful to Leverhulme Trust project RPG-2018-063 for partial support of this research.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prilepsky, E.D., Prilepsky, J.E. Estimation of Optimal Parameter of Regularization of Signal Recovery. Radioelectron.Commun.Syst. 61, 406–418 (2018). https://doi.org/10.3103/S0735272718090030

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0735272718090030

Navigation