On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary

Among the many ways to model signals, a recent approach that draws considerable attention is sparse representation modeling. In this model, the signal is assumed to be generated as a random linear combination of a few atoms from a pre-specified dictionary. In this work we analyze two Bayesian denoising algorithms

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Javier Turek, Irad Yavneh, Matan Protter, Michael Elad
• Keywords: Bayesian Denoising Algorithms, CORR 2010, Education, Estimation Errors, Sparse Representation Modeling