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AN ITERATIVE LAGRANGE MULTIPLIER METHOD FOR CONSTRAINED TOTAL-VARIATION-BASED IMAGE DENOISING.
- Source :
-
SIAM Journal on Numerical Analysis . 2012, Vol. 50 Issue 3, p983-1003. 21p. - Publication Year :
- 2012
-
Abstract
- Various effective algorithms have been proposed in the past two decades for nonlinear PDEs arising from the unconstrained total-variation-based image denoising problem regularizing the total variation constrained minimization model. Such algorithms can be used to obtain a satisfactory result as long as a suitable regularization parameter balancing the trade-off between a good fit to the data and a regular solution is given. However, it is generally difficult to obtain a suitable regularization parameter without which restored images can be unsatisfactory: if it is too large, then the resulting solution is still contaminated by noise, while if too small, the solution is a poor approximation of the true noise-free solution. To provide an automatic method for the regularization parameter when the noise level is known a priori, one way is to address the coupled Karush--Kuhn-- Tucker (KKT) systems from the constrained total variation optimization problem. So far much less work has been done on this problem. This paper presents an iterative update algorithm for a Lagrange multiplier to solve the KKT conditions, and our proposed method can adaptively deal with noisy images with different variances δ². Numerical experiments show that our model can effectively find a highly accurate solution and produce excellent restoration results in terms of image quality. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00361429
- Volume :
- 50
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- SIAM Journal on Numerical Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 82155415
- Full Text :
- https://doi.org/10.1137/110829209