Skip to main content
SpringerLink
Log in

3D Image Sharpening by Grid Warping

  • Conference paper
  • First Online:
Intelligence Science and Big Data Engineering. Image and Video Data Engineering (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9242))

Abstract

A method for sharpening of 3D volume images has been developed. The idea of the proposed algorithm is to transform the 3D neighborhood of the edge so that the neighboring pixels move closer to the edge, and then resample the image from the warped grid to the original pixel grid. The proposed technique preserves image textures while making the edges sharper. The effectiveness of the proposed method is demonstrated with synthetic volume images and real micro CT images.

The work was supported by Russian Science Foundation grant 14-11-00308.

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 EPUB and 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

References

  1. Almeida, M., Figueiredo, M.: Parameter estimation for blind and non-blind deblurring using residual whiteness measures. IEEE Trans. Image Process. 22, 2751–2763 (2013)

    Article  MathSciNet  Google Scholar 

  2. Arad, N., Gotsman, C.: Enhancement by image-dependent warping. IEEE Trans. Image Proc. 8, 1063–1074 (1999)

    Article  Google Scholar 

  3. Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational bayesian blind deconvolution using a total variation prior. IEEE Trans. Image Process. 18, 12–26 (2009)

    Article  MathSciNet  Google Scholar 

  4. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)

    Article  Google Scholar 

  5. Conte, F., Germani, A., Iannello, G.: A Kalman filter approach for denoising and deblurring 3-D microscopy images. IEEE Trans. Image Process. 22(12), 5306–5321 (2013)

    Article  Google Scholar 

  6. Gilboa, G., Sochen, N.A., Zeevi, Y.Y.: Regularized shock filters and complex diffusion. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 399–413. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Gui, Z., Liu, Y.: An image sharpening algorithm based on fuzzy logic. Optik - Int. J. Light Electron Opt. 122(8), 697–702 (2011)

    Article  Google Scholar 

  8. Prades-Nebot, J., et al.: Image enhancement using warping technique. IEEE Electron. Lett. 39, 32–33 (2003)

    Article  Google Scholar 

  9. Krylov, A., Nasonova, A., Nasonov, A.: Grid warping for image sharpening using one-dimensional approach. In: Proceedings of International Conference on Signal Processing (ICSP2014), pp. 672–677. IEEE, Hangzhou (2014)

    Google Scholar 

  10. Nasonova, A., Krylov, A.: Deblurred images post-processing by Poisson warping. IEEE Signal Process. Lett. 22(4), 417–420 (2015)

    Article  Google Scholar 

  11. Nasonova, A., Nasonov, A., Krylov, A., Pechenko, I., Umnov, A., Makhneva, N.: Image warping in dermatological image hair removal. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014, Part II. LNCS, vol. 8815, pp. 159–166. Springer, Heidelberg (2014)

    Google Scholar 

  12. Nasonova, A.A., Krylov, A.S.: Determination of image edge width by unsharp masking. Comput. Math. Model. 25, 72–78 (2014)

    Article  Google Scholar 

  13. Oliveira, J., Bioucas-Dia, J.M., Figueiredo, M.: Adaptive total variation image deblurring: a majorization-minimization approach. Signal Process. 89, 1683–1693 (2009)

    Article  MATH  Google Scholar 

  14. Osher, S., Rudin, L.I.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27(4), 919–940 (1999)

    Article  MATH  Google Scholar 

  15. Ramponi, G.: A cubic unsharp masking technique for contrast enhancement. Signal Process. 67, 211–222 (1998)

    Article  MATH  Google Scholar 

  16. Ramponi, G., Strobel, N., Mitra, S., Yu, T.: Nonlinear unsharp masking methods for image contrast enhancement. J. Electron. Imaging 5, 353–366 (1996)

    Article  Google Scholar 

  17. Schavemaker, J., Reinders, M., Gerbrands, J., Backer, E.: Image sharpening by morphological filtering. Pattern Recognit. 33(6), 997–1012 (2000)

    Article  Google Scholar 

  18. Shimura, A., Taguchi, A.: Digital image interpolation with warping of coordinate point and biasing of signal amplitude. Electron. Commun. Jpn 88, 10–21 (2005)

    Article  Google Scholar 

  19. Weickert, J.: Coherence-enhancing shock filters. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 1–8. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Laboratory of Mathematical Methods of Image Processing, Lomonosov Moscow State University, Moscow, Russia

    Andrey S. Krylov & Andrey V. Nasonov

Authors
  1. Andrey S. Krylov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrey V. Nasonov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrey S. Krylov .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Shaanxi, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Krylov, A.S., Nasonov, A.V. (2015). 3D Image Sharpening by Grid Warping. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23989-7_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23987-3

  • Online ISBN: 978-3-319-23989-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation