Skip to main content
SpringerLink
Log in

Recognition of Images Degraded by Gaussian Blur

  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9256))

Included in the following conference series:

Abstract

We introduce a new theory of invariants to Gaussian blur. The invariants are defined in Fourier spectral domain by means of projection operators and, equivalently, in the image domain by means of image moments. The application of these invariants is in blur-invariant image comparison and recognition. The behavior of the invariants is studied and compared with other methods in experiments on both artificial and real blurred and noisy images.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Carasso, A.S.: The APEX method in image sharpening and the use of low exponent Lévy stable laws. SIAM Journal on Applied Mathematics 63(2), 593–618 (2003)

    Article  MathSciNet  Google Scholar 

  2. Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(7), 699–716 (1998)

    Article  Google Scholar 

  3. Flusser, J., Suk, T.: Degraded image analysis: An invariant approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6), 590–603 (1998)

    Article  Google Scholar 

  4. Flusser, J., Suk, T., Boldyš, J., Zitová, B.: Projection operators and moment invariants to image blurring. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(4), 786–802 (2015)

    Article  Google Scholar 

  5. Gopalan, R., Taheri, S., Turaga, P., Chellappa, R.: A blur-robust descriptor with applications to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(6), 1220–1226 (2012)

    Article  Google Scholar 

  6. Honarvar Shakibaei, B., Flusser, J.: Image deconvolution in moment domain. In: Papakostas, G.A. (ed.) Moments and Moment Invariants - Theory and Applications Gate to Computer Science and Research, vol. 1, pp. 111–125. Science Gate Publishing (2014)

    Google Scholar 

  7. Liu, J., Zhang, T.: Recognition of the blurred image by complex moment invariants. Pattern Recognition Letters 26(8), 1128–1138 (2005)

    Article  Google Scholar 

  8. Šroubek, F., Flusser, J.: Multichannel blind iterative image restoration. IEEE Transactions on Image Processing 12(9), 1094–1106 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  9. Xiao, B., Ma, J.F., Cui, J.T.: Combined blur, translation, scale and rotation invariant image recognition by Radon and pseudo-Fourier-Mellin transforms. Pattern Recognition 45, 314–321 (2012)

    Article  MATH  Google Scholar 

  10. Xue, F., Blu, T.: A novel SURE-based criterion for parametric PSF estimation. IEEE Transactions on Image Processing 24(2), 595–607 (2015)

    Article  MathSciNet  Google Scholar 

  11. Zhang, W., Cham, W.K.: Single-image refocusing and defocusing. IEEE Transactions on Image Processing 21(2), 873–882 (2012)

    Article  MathSciNet  Google Scholar 

  12. Zhang, Z., Klassen, E., Srivastava, A.: Gaussian blurring-invariant comparison of signals and images. IEEE Transactions on Image Processing 22(8), 3145–3157 (2013)

    Article  Google Scholar 

  13. Zhang, Z., Klassen, E., Srivastava, A., Turaga, P., Chellappa, R.: Blurring-invariant riemannian metrics for comparing signals and images. In: IEEE International Conference on Computer Vision, ICCV 2011, pp. 1770–1775 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation of the CAS, Pod Vodárenskou věží 4, 182 08, Praha 8, Czech Republic

    Jan Flusser, Tomáš Suk, Sajad Farokhi & Cyril Höschl IV

Authors
  1. Jan Flusser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomáš Suk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sajad Farokhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cyril Höschl IV
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomáš Suk .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Flusser, J., Suk, T., Farokhi, S., Höschl, C. (2015). Recognition of Images Degraded by Gaussian Blur. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23192-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23191-4

  • Online ISBN: 978-3-319-23192-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation