Blind deconvolution of images using optimal sparse representations

被引:64
作者
Bronstein, MM [1 ]
Bronstein, AM
Zibulevsky, M
Zeevi, YY
机构
[1] Technion Israel Inst Technol, Dept Comp Sci, IL-32000 Haifa, Israel
[2] Technion Israel Inst Technol, Dept Elect Engn, IL-32000 Haifa, Israel
关键词
blind deconvolution; quasi-maximum likelihood; relative Newton optimization; sparse representations;
D O I
10.1109/TIP.2005.847322
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:726 / 736
页数:11
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