title: Blind deconvolution of images using optimal sparse representations.
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.
authors.affiliation: Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. bronstein@ieee.org
authors.forename: Michael M
authors.initials: MM
authors.lastname: Bronstein
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Bronstein
authors.forename: Michael
authors.initials: M
authors.lastname: Zibulevsky
authors.forename: Yehoshua Y
authors.initials: YY
authors.lastname: Zeevi
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis; D013223:Statistics as Topic