PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Bedini L., Del Corso G., Tonazzini A. A preconditioning technique for edge-preserving image restoration. In: 1999 International Conference on Information Intelligence and Systems. Proceedings (Bethesda, Maryland, USA, October 31-November 3, 1999), 519-526. IEEE Computer Society, 1999. 1999.
 
 
Abstract
(English)
Preconditioned conjugate gradient algorithms have been successfully used to significantly reduce the number of iterations in Tikhonov regularization techniques, for image restoration. Nevertheless, in many cases Tikhonov regularization is inadequate, in that it produces images that are oversmoothed across intensity edges. Edge-preserving regularization can overcome this inconvenience but has a higher complexity. In this paper we show how the use of preconditioners can improve the computational performance of edge-preserving image restoration as well. In particular, we adopt an image model which explicitly accounts for a constrained binary line process, and a mixedannealing algorithm that alternates steps of stochastic updating of the lines with steps of conjugate gradient-based estimation of the intensity. The presence of the line process requires a specific preconditioning strategy to manage the particular structure of the matrix of the equivalent least squares problem. Experimental results are provided to show the satisfactory performance of the method, both with respect to the quality of the restored images and the computational saving.
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