Fast relative Newton algorithm for blind deconvolution of images

Alexander M. Bronstein*, Michael Zibulevsky, Yehoshua Y. Zeevi, Michael M. Bronstein

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

We present an efficient Newton-like algorithm for quasi-maximum likelihood (QML) blind deconvolution of images. This algorithm exploits the sparse structure of the Hessian. An optimal distribution-shaping approach by means of sparsification allows one to use simple and convenient sparsity prior for processing of a wide range of natural images. Simulation results demonstrate the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)1233-1236
Number of pages4
JournalProceedings - International Conference on Image Processing, ICIP
Volume5
StatePublished - 2004
Externally publishedYes
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: 18 Oct 200421 Oct 2004

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