Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Deducing Local Influence Neighbourhoods with Application to Edge-Preserving Image Denoising

  • Conference paper

Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 4538))

  • 1354Accesses

Abstract

Traditional image models enforce global smoothness, and more recently Markovian Field priors. Unfortunately global models are inadequate to represent the spatially varying nature of most images, which are much better modeled as piecewise smooth. This paper advocates the concept of local influence neighbourhoods (LINs). The influence neighbourhood of a pixel is defined as the set of neighbouring pixels which have a causal influence on it. LINs can therefore be used as a part of the prior model for Bayesian denoising, deblurring and restoration. Using LINs in prior models can be superior to pixel-based statistical models since they provide higher order information about the local image statistics. LINs are also useful as a tool for higher level tasks like image segmentation. We propose a fast graph cut based algorithm for obtaining optimal influence neighbourhoods, and show how to use them for local filtering operations. Then we present a new expectation-maximization algorithm to perform locally optimal Bayesian denoising. Our results compare favourably with existing denoising methods.

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

Access this chapter

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Keys, R.G.: Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing 29(6), 1153–1160 (1981)

    Article MATH MathSciNet  Google Scholar 

  2. Meijering, E.: A chronology of interpolation. Proceedings of the IEEE 90(3), 319–342 (2002)

    Article  Google Scholar 

  3. Lee, S., Paik, J.: Image interpolation using adaptive fast b-spline filtering. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 177–180 (1993)

    Google Scholar 

  4. Erler, K., Jernigan, E.: Adaptive recursive image filtering. In: Proceedings, SPIE, pp. 3017–3021 ( 1991)

    Google Scholar 

  5. Rand, K., Unbehauen, R.: An adaptive recursive 2-d filter for removal of gaussian noise in images. IEEE Transactions on Image Processing 1, 431–436 (1992)

    Article  Google Scholar 

  6. Li, S.: Markov Random Field Modeling in Computer Vision. Springer, Heidelberg (1995)

    Google Scholar 

  7. Schultz, R., Stevenson, R.: A bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing 3(3), 233–242 (1994)

    Article  Google Scholar 

  8. Debayle, J., Pinoli, J.C.: Multiscale image filtering and segmentation by means of adaptive neighbourhood mathematical morphology. IEEE International Conference on Image Processing 3, 537–540 (2005)

    Google Scholar 

  9. Boykov, Y., Veksler, O., Zabih, R.: A variable window approach to early vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1283–1294 (1998) An earlier version of this work appeared in CVPR 1997

    Google Scholar 

  10. Smits, P.C., Dellepiane, S.G.: Synthetic aperture radar image segmentation by a detail preserving markov random field approach. IEEE Transactions on Geoscience and Remote Sensing 35(4), 844–857 (1997)

    Article  Google Scholar 

  11. Puetter, R.: Pixon-based multiresolution image reconstruction and the quantification of picture information content. International Journal of Image Systems and Technologies 6, 314–331 (1995)

    Article  Google Scholar 

  12. Smith, S.M., Brady, J.M.: Susan a new approach to low level image processing. International Journal of Computer Vision 23(1), 45–78 (1997)

    Article  Google Scholar 

  13. Descombes, X., Kruggel, F.: A markov pixon information approach for low-level image description. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(6), 482–494 (1999)

    Article  Google Scholar 

  14. Ren, X., Malik, J.: Learning a classification model for segmentation. In: International Conference on Computer Vision (2003)

    Google Scholar 

  15. Ahuja, N., Davis, L.S., Milgram, D.L., Rosenfeld, A.: Piecewise approximation of pictures using maximal neighbourhoods. IEEE Transactions on Computation 27, 375–379 (1978)

    Article  Google Scholar 

  16. Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 165–168 (1980)

    Article  Google Scholar 

  17. Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)

    Article  Google Scholar 

  18. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 288–305 (1993)

    Google Scholar 

  19. Saha, P.K., Udupa, J.K.: Scale-based diffusive image filtering preserving boundary sharpness and fine structures. IEEE Transactions on Medical Imaging 20(11), 1140–1155 (2001)

    Article  Google Scholar 

  20. Paranjape, R.B., Rangayyan, R.M.: Adaptive neighbourhood mean and median image filtering. Electronic Imaging 3(4), 360–367 (1994)

    Article  Google Scholar 

  21. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  22. Kostas, H., Serafim, N.: Hybrid image segmentation using watersheds and fast region merging. IEEE Transactions on Image Processing 7(12), 1684–1699 (1998)

    Article  Google Scholar 

  23. Zhong, X., Dinggang, S., Davatzikos, C.: Determining correspondence in 3-d mr brain images using attribute vectors as morphological signatures of voxels. IEEE Transactions on Medical Imaging 23(10), 1276–1291 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Radiology, UCSF, San Fransisco, USA

    Ashish Raj & Karl Young

  2. Industrial Research Limited, Wellington, New Zealand

    Kailash Thakur

Authors
  1. Ashish Raj

    You can also search for this author inPubMed Google Scholar

  2. Karl Young

    You can also search for this author inPubMed Google Scholar

  3. Kailash Thakur

    You can also search for this author inPubMed Google Scholar

Editor information

Francisco Escolano Mario Vento

Rights and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Raj, A., Young, K., Thakur, K. (2007). Deducing Local Influence Neighbourhoods with Application to Edge-Preserving Image Denoising. In: Escolano, F., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2007. Lecture Notes in Computer Science, vol 4538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72903-7_17

Download citation

Publish with us


[8]ページ先頭

©2009-2025 Movatter.jp