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 in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows blind deconvolution of sources with arbitrary distribution, and show how to find optimal sparsifying transformations by training. © Springer-Verlag 2004.
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CITATION STYLE
Bronstein, A. M., Bronstein, M. M., Zibulevsky, M., & Zeevi, Y. Y. (2004). Optimal sparse representations for blind deconvolution of images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 500–507. https://doi.org/10.1007/978-3-540-30110-3_64
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