Blind deconvolution of images using optimal sparse representations.

Michael M Bronstein; Alexander M Bronstein; Michael Zibulevsky; Yehoshua Y Zeevi

doi:10.1109/tip.2005.847322

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Blind deconvolution of images using optimal sparse representations.

Michael M Bronstein·Alexander M Bronstein·Michael Zibulevsky·Yehoshua Y Zeevi

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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.

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Algorithms Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Pattern Recognition, Automated Regression Analysis Statistics as Topic Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Michael M Bronstein, Alexander M Bronstein, Michael Zibulevsky, Yehoshua Y Zeevi. 2005. Blind deconvolution of images using optimal sparse representations.. https://doi.org/10.1109/tip.2005.847322

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