Skip to main content
SpringerLink
Log in

Deconvolution filter design of transmission channel: application to 3D objects using features extraction from orthogonal descriptor

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The proposed work focuses on new transmission method of 3D objects modelized by Roesser local state-space model and moment theory which is considered as an excellent descriptor for 3D objects. The orthogonal Hahn polynomial with optimal parameters and their moment correspond are used to generate the features extraction of 3D objects to generated the input system according to the order defined in advance. Practically this method is based on two pillars. The first is the exact selection of Hahn polynomial parameters for \(\alpha = 20, \beta = 0\) to take full advantage of the benefits in order to build moment matrix. The second one is the correlation of these generated matrices with Roesser local state-space model for transmitting the features vectors instead the full 3D object with, and without noises. As a result, the mean square error curve has been used to measure the performance of the proposed method. The PSNR curve is used to evaluate the validity of the proposed model. Finally, we show that the computational cost of \(\hbox {ETIR} = 80\%\) for the transmission, and comparison of the proposed approach with different kinds of noises generated by the environment.

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

Similar content being viewed by others

References

  1. McNally JG, Karpova T, Cooper J, Conchello JA (1999) Three-dimensional imaging by deconvolution microscopy. Methods 19(3):373–385

    Article  Google Scholar 

  2. Sarder P, Nehorai A (2006) Deconvolution methods for 3D fluorescence microscopy images. IEEE Signal Process Mag 23(3):32–45

    Article  Google Scholar 

  3. Griffa A, Garin N, Sage D (2010) Comparison of deconvolution software in 3D microscopy. A user point of view part 1, G.I.T. Imag. Microsc. 1:43–45

    Google Scholar 

  4. Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. Adv Neural Inf Process Syst 1790–1798

  5. Patwary N, Preza C (2015) Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depthvariant point-spread functions. Biomed Opt Express 6(10):3826–3841

    Article  Google Scholar 

  6. Brailean JC, Kleihorst RP, Efstratiadis S et al (1995) Noise reduction filters for dynamic image sequences: a review. Proc IEEE 83:1272–1291

    Article  Google Scholar 

  7. Cho ZH, Burger JR (1977) Construction, restoration, and enhancement of 2 and 3Dimensional images. IEEE Trans Nuclear Sci 24:886–892

    Article  Google Scholar 

  8. Tsui ET, Budinger TF (1979) A stochastic filter for transverse section reconstruction. IEEE Trans Nuclear Sci 26:2687–2690

    Article  Google Scholar 

  9. Ponti-Jr MP, Mascarenhas NDA, Ferreira PJSG et al (2013) Three-dimensional noisy image restoration using filtered extrapolation and deconvolution. SIViP 7:1–10. https://doi.org/10.1007/s11760-011-0216-x

    Article  Google Scholar 

  10. Sheppard C, Gu M, Kawata Y, Kawata S (1994) Three-dimensional transfer functions for high-aperture systems. J Opt Soc Am 11(2):593–598

    Article  Google Scholar 

  11. Sarder P, Nehorai A (2006) Deconvolution methods for 3D fluorescence microscopy images. IEEE Signal Process Mag 23(3):32–45

    Article  Google Scholar 

  12. Miraut D, Portilla J (2012) Efficient shift-variant image restoration using deformable filtering (Part I). EURASIP J Adv Signal Process 2012:100

    Article  Google Scholar 

  13. Kririm S, Hmamed A (2015) Robust \(H_{\infty }\) filtering for uncertain differential linear repetitive processes via LMIs and polynomial matrices. WSEAS Trans Syst Control 10:396–403

    Google Scholar 

  14. Kririm S, Hmamed A, Tadeo F (2016) Robust \(H_{\infty }\) Filtering for uncertain 2D singular Roesser models. Circuits Syst Signal Process 34(7):2213–2235

    Article  MathSciNet  Google Scholar 

  15. Kririm S, Hmamed A (2019) Robust \(H_{\infty }\) filtering for uncertain 2D singular systems with delays. In: 8th International conference on systems and control (ICSC)

  16. Chen Y, Yan J, Feng J, Sareh P (2021) PSO-based metaheuristic design generation of non-trivial flat-foldable origami tessellations with degree-4 vertices. J Mech Des Trans ASM 143(1):011703

    Article  Google Scholar 

  17. Koohestani K (2011) An orthogonal self-stress matrix for efficient analysis of cyclically symmetric space truss structures via force method. Int J Solids Struct 48(2):227–233. https://doi.org/10.1016/j.ijsolstr.2010.09.023

    Article  MATH  Google Scholar 

  18. Chen Y, Sareh P, Feng J, Sun Q (2017) A computational method for automated detection of engineering structures with cyclic symmetries. Comput Struct 191:153–6. https://doi.org/10.1016/j.compstruc.2017.06.013

    Article  Google Scholar 

  19. Tzafestas SG, Pimenides TG (1982) Exact model-matching control of three-dimensional systems using state and output feedback. Int J Syst Sci 13(11):1171–1187. https://doi.org/10.1080/00207728208926421

    Article  MATH  Google Scholar 

  20. Feng ZY, Xu L, Wu Q (2012) \(H_{\infty }\) control of linear multidimensional discrete systems. Multidimens Syst Signal Process 23:381–411

    Article  MathSciNet  Google Scholar 

  21. Galkowski K, Lam J, Xu S, Lin Z (2003) LMI approach to state-feedback stabilization of multidimensional systems. Int J Control 76:1428–1436

    Article  MathSciNet  Google Scholar 

  22. Poczekajlo P, Wawryn K (2018) Algorithm for realisation, parameter analysis, and measurement of pipelined separable 3D finite impulse response filters composed of givens rotation structures. IET Signal Process 12(7):857–867

    Article  Google Scholar 

  23. Anderson BDO, Moore JB (1979) Optimal filtering. Prentice-Hall, Englewood

    MATH  Google Scholar 

  24. Sturm JF (1999) Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optim Methods Softw 11(1):625–653

    Article  MathSciNet  Google Scholar 

  25. Wu L, Lam J, Paszke W, Galkowski K, Rogers E (2008) Robust \(H\infty\) filtering for uncertain differential linear repetitive processes. Int J Adapt Control Signal Process 22:243–265

    Article  MathSciNet  Google Scholar 

  26. Oliveira RCLF, Peres PLD (2007) Parameter-dependent LMIs in robust analysis: characterization of homogeneous polynomially parameter-dependent solution via LMI relaxations. IEEE Trans Autom Control 52(7):1334–1340

    Article  MathSciNet  Google Scholar 

  27. Galkowski K, Rogers E, Paszke W, Owens DH (2003) Linear repetitive processes control theory applied to a physical example. Appl Math Comput Sci 13(1):87–99

    MathSciNet  MATH  Google Scholar 

  28. Galkowski K, Paszke W, Lam J, Xu S, Owens DH (2003) Stability and control of differential linear repetitive processes using an LMI setting. IEEE Trans Circuits Syst II Analog Digital Signal Process 50(9):662–666

    Article  Google Scholar 

  29. Dabkowski P, Galkowski K, Datta B, Rogers E (2010) LMI based stability and stabilization of second-order linear repetitive processes. Asian J Control 12(2):136–145

    Article  MathSciNet  Google Scholar 

  30. El Mallahi M, Zouhri A, Amakdouf H, Qjidaa H (2018) Rotation scaling and translation invariants of 3D radial shifted legendre moments. Int J Autom Comput 15(2):169–180

    Article  Google Scholar 

  31. El Mallahi M (2017) Three dimensional radial Tchebichef moment invariants for volumetric image recognition. Pattern Recognit Image Anal

  32. Amakdouf H, Zouhri A, El Mallahi M, Tahiri A, Qjidaa H (2018) Translation scaling and rotation invariants of 3D Krawtchouk moments. In: International conference on intelligent systems and computer vision, ISCV, INSPEC Accession Number. 17737764

  33. El Mallahi M, Zouhri A, EL-mekkaoui J, Qjidaa H (2017) Three dimensional radial Krawtchouk moment invariants for volumetric image recognition. Pattern Recognit Image Anal 27(4):810–824

  34. Amakdouf H, Zouhri A, El Mallahi M et al (2020) Color image analysis of quaternion discrete radial Krawtchouk moments. Multimed Tools Appl 79:26571–26586. https://doi.org/10.1007/s11042-020-09120-0

  35. El Mallahi M, Mesbah A, Qjidaa H (2018) 3D radial invariant of dual Hahn moments. Neural Comput Appl 30(7):2283–2294

    Article  Google Scholar 

  36. Mesbah A, Zouhri A, El Mallahi M, Qjidaa H (2017) Robust reconstruction and generalized dual Hahn moments invariants extraction for 3D images. 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg, 8(7)

  37. El Mallahi M, Zouhri A, Mesbah A, El Affar I, Qjidaa H (2018) Radial invariant of 2D and 3D Racah moments. Multimedia Tools Appl Int J 77(6):6583–6604

    Article  Google Scholar 

  38. Amakdouf H, Zouhri A, El Mallahi M et al (2021) Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments. Multimedia Tools Appl 80:3173–3192. https://doi.org/10.1007/s11042-020-09781-x

    Article  Google Scholar 

  39. Xiao G, Li J, Chen Y, Li K (2020) MalFCS: an effective malware classification framework with automated feature extraction based on deep convolutional neural networks. J Parallel Distrib Comput 141:49–58. https://doi.org/10.1016/j.jpdc.2020.03.012

    Article  Google Scholar 

  40. Roesser RP (1975) A discrete state-space model for linear image processing. IEEE Trans Autom Control 20:1–10

    Article  MathSciNet  Google Scholar 

  41. de Oliveira MC, Skelton RE (2001) Stability tests for constrained linear systems. In: Reza Moheimani SO (Ed.) Perspectives in robust control of lecture notes in control and information science, vol. 268. Springer, New York, pp 241–257

  42. Xu L, Fan H, Lin Z, Bose NK (2008) A direct-construction approach to multidimensional realization and LFR uncertainty modeling. Multidimens Syst Signal Process 19:323–359

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Higher School of Technology Guelmim, Ibn Zohr University, Agadir, Morocco

    Said Kririm

  2. Computer Science Signals Automation and Cognitivism Laboratory, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Amal Zouhri, Hassan Qjidaa & Abdelaziz Hmamed

Authors
  1. Said Kririm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amal Zouhri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Qjidaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdelaziz Hmamed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amal Zouhri.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Kririm, S., Zouhri, A., Qjidaa, H. et al. Deconvolution filter design of transmission channel: application to 3D objects using features extraction from orthogonal descriptor. Neural Comput & Applic 33, 16865–16879 (2021). https://doi.org/10.1007/s00521-021-06533-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06533-2

Keywords

Mathematics Subject Classification

Search

Navigation