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NUMERICAL EXPERIMENTS IN IMAGE RECONSTRUCTION BY USING TOTAL VARIATION REGULARIZATION

G. Dimitriu

First published: 2008DOI pendingView metrics

Abstract

Total variation (TV) minimization represents an example of a class of recently introduced image processing techniques that use partial differential equations. Total variation has been shown to be very successful in many image processing applications. The reason for using TV is that it has been shown to suppress noise effectively while capturing sharp edges. The aim of the study is to present some numerical results for the image reconstruction process by using a computational algorithm which incorporates the TV regularization.

Publication details

Title
NUMERICAL EXPERIMENTS IN IMAGE RECONSTRUCTION BY USING TOTAL VARIATION REGULARIZATION
Authors
G. Dimitriu
Proceedings
8th International Scientific Conference - SGEM2008
Publisher
SGEM Scientific GeoConference
Year
2008
Pages
605-613
ISSN
Not available yet
ISBN
954-91818-1-2
Language
en
Publication type
Conference Paper
Keywords
TV regularizationimage reconstructionpartial differential equationsharp edge
References9

  1. Blomgren P. & Chan T.F. & Mulet P. & Wong CK. Total Variation Image Restoration: Numerical methods and Extension, IEEE International Conference on Image Processing, Vol. 3, pp. 384-387.

  2. Chan T.F. & Golub G.H. & Mulet P. A nonlinear primal-dual method for total variation based image restoration, ICAOS'96, 12th international conference on Analysis and Optimization of systems: Images, wavelets and PDE's, Paris, pp. 241252, June 1996.

  3. Chan T.F. & Mulet P. On the convergence of the lagged diflusivity fixed point method in total variation image restoration, SIAM Journal on Numerical Analysis, Vol. 36, pp. 354-367, 1999.

  4. Dobson D.C & Santosa F. Recovery of Blocky Images from Noisy and Blurred Data, SIAM Journal on Applied Mathematics, Vol. 56, Issue 4, pp. 1181-1198, Aug. 1996.

  5. Hanke M. & Hansen P.C. Regularization methods for large-scale problems, Survey on Mathematics for Industry ; Vol. 3, Issue 4, pp. 253-315, 1993.

  6. Rudin L.I. & Osher S. & Fatemi E. Nonlinear total variation based noise removal algorithms, Proceeding of the 11th Annual International Conference of the center for Nonlinear Studies, Physica D, Vol. 60, pp. 259-268, Nov. 1992.

  7. Strong D.M. Adaptive Total Variation Minimizing Image Restoration, PhD Dissertation, University of California, LA 1997.

  8. Strong D.M. & Chan T.F. Relation of Regularization Parameter and Scale in Total Variation based Image Denoising, June 1995.

  9. Vogel CR. A fast, robust algorithm for total variation based reconstruction of noisy, blurred images. IEEE Transaction of Image Processing, Vol. 7, pp. 813-824, July 1998.

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