CFP last date
20 January 2025
Reseach Article

Implementation of Improved Realtime Offline Image Filtering Method by Autocorrelation Function

Published on April 2012 by M. A. Gopisaran, S. S. Sreeja Mole
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 4
April 2012
Authors: M. A. Gopisaran, S. S. Sreeja Mole
918499d8-0828-44b3-8bf1-1c646308adde

M. A. Gopisaran, S. S. Sreeja Mole . Implementation of Improved Realtime Offline Image Filtering Method by Autocorrelation Function. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 4 (April 2012), 1-5.

@article{
author = { M. A. Gopisaran, S. S. Sreeja Mole },
title = { Implementation of Improved Realtime Offline Image Filtering Method by Autocorrelation Function },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 4 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/icon3c/number4/6024-1025/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A M. A. Gopisaran
%A S. S. Sreeja Mole
%T Implementation of Improved Realtime Offline Image Filtering Method by Autocorrelation Function
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 4
%P 1-5
%D 2012
%I International Journal of Computer Applications
Abstract

A novel scheme for anisotropic diffusion driven by the image autocorrelation function is implemented. The diffusion tensor field is estimate by autocorrelation and computation from a scalar product of diffusion tensor and the image Hessian functions obtains an evolution equation. For a minimized spatial support for a hessian a set of filters are proposed. The filtering method performs favorable in many cases in particularly at low noise levels. A real time performance is easily achieved in a GPU implementation.

References
  1. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images,"in Proc. 6th ICCV, 1998, pp. 836–846.
  2. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Trans. Pattern Analysis and Machine Intell. , vol. 17, no. 8, pp. 90–799, Aug. 1995.
  3. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 24, no. 5, pp. 603–619, Apr. 2002.
  4. M. Felsberg and G. Granlund, "Anisotropic channel filtering," in Proc. 13th Scand. Conf. Image Anal. , 2003, pp. 755–762, ser. LNCS 2749.
  5. J. Portilla, V. Strela, J. Wainwright, and E. P. Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain," IEEE Trans. Image Process. , vol. 12, no. 11, pp. 1338–1351, Nov. 2003.
  6. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process. ,vol. 15, no. 12, pp. 3736–3745, Nov. 2006.
  7. R. A. Carmona and S. Zhong, "Adaptive smoothing respecting feature directions," IEEE Trans. Image Process. , vol. 7, no. 3, pp. 353–358, Mar. 1998.
  8. M. Felsberg, "On the relation between anisotropic diffusion and iterated adaptive filtering," in Proc. DAGMSymp. Mustererkennung, 2008,vol. 5096, pp. 436–445.
  9. J. Bigün and G. H. Granlund, "Optimal orientation detection of linear symmetry," in Proc. IEEE 1st Int. Conf. Comput. Vis. , London, U. K. , Jun. 1987, pp. 433–438.
  10. W. Förstner and E. Gülch, "A fast operator for detection and precise location of distinct points, corners and centres of circular features," in Proc. ISPRS Intercommission Workshop, Interlaken, Switzerland, Jun. 1987, pp. 149–155.
  11. J. Weickert, "A review of nonlinear diffusion filtering," in Scale-Space Theory in Computer Vision, ser. LNCS, B. ter Haar Romeny, L. Florack, J. Koenderink, and M. Viergever, Eds. Berlin, Germany: Springer, 1997, vol. 1252, pp. 260–271.
  12. W. T. Freeman and E. H. Adelson, "The design and use of steerable filters," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 13, no. 9, pp. 891–906, Sep. 1991.
  13. H. Knutsson, R. Wilson, and G. H. Granlund, "Anisotropic nonstationary image estimation and its applications: Part I—Restoration of noisy images," IEEE Trans. Commun. , vol. COM-31, no. 3, pp. 388–397, Mar. 1983.
  14. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 12, no. 7, pp. 629–639, Jul. 1990.
  15. J. Weickert, "Theoretical foundations of anisotropic diffusion in image processing," Computing, Suppl. , vol. 11, pp. 221–236, 1996.
  16. R. van den Boomgaard, "Nonlinear diffusion in computer vision," Jan. 28, 2008 [Online]. Available: http://staff. science. uva. nl/rein/nldiffusionweb/material. html.
  17. J. Weickert, K. Zuiderveld, B. ter Haar Romeny, and W. Niessen, "Parallel implementations of AOS schemes: A fast way of nonlinear diffusion filtering," in Proc. IEEE Int. Conf. Image Process. , 1997, pp. 396–399.
  18. M. Welk, J. Weickert, and G. Steidl, "From tensor-driven diffusion to anisotropic wavelet shrinkage," in Proc. Eur. Conf. Comput. Vis. , 2006, vol. 3951, pp. 391–403.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Filtering Diffusion Filtering Image Enhancement Steerable Filters Structure Tensor