Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors
IEEE Transactions on Image Processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Sparse source separation of non-instantaneous spatially varying single path mixtures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Local object-based super-resolution mosaicing from low-resolution video
Signal Processing
Enhancement of source independence for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Sparse deflations in blind signal separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
On separation of semitransparent dynamic images from static background
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Non-negative matrix factorization approach to blind image deconvolution
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
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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.