Blind deconvolution of images using optimal sparse representations

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The relative Newton algorithm, previously proposed for quasi-maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used as the nonlinear term for sparse sources. In addition, we propose a method of sparsification, which allows blind deconvolution of arbitrary sources, and show how to find optimal sparsifying transformations by supervised learning.

Original languageEnglish
Pages (from-to)726-736
Number of pages11
JournalIEEE Transactions on Image Processing
Volume14
Issue number6
DOIs
StatePublished - Jun 2005
Externally publishedYes

Funding

FundersFunder number
Ollendorff Minerva Center
European Commission

    Keywords

    • Blind deconvolution
    • Quasi-maximum likelihood
    • Relative Newton optimization
    • Sparse representations

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