Blind deconvolution of images using optimal sparse representations.
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.
Bronstein
Michael M
MM
Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. bronstein@ieee.org
Bronstein
Alexander M
AM
Zibulevsky
Michael
M
Zeevi
Yehoshua Y
YY
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