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Blind deconvolution of images using optimal sparse representations

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4 Author(s)
M. M. Bronstein ; Dept. of Comput. Sci., Technion - Israel Inst. of Technol., Haifa, Israel ; A. M. Bronstein ; M. Zibulevsky ; Y. Y. Zeevi

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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IEEE Transactions on Image Processing  (Volume:14 ,  Issue: 6 )