Learning redundant dictionaries with translation invariance property: the MoTIF algorithm
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This paper introduces an algorithm for sparse approximation in redundant dictionaries, called the M-Term Pursuit (MTP), based on the matching pursuit approach (MP). This algorithm decomposes the signal into a linear combination of selected atoms, chosen to ...
Approximating a signal or an image with a sparse linear expansion from an overcomplete dictionary of atoms is an extremely useful tool to solve many signal processing problems. Finding the sparsest approximation of a signal from an arbitrary dictionary is ...
The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictio- nary of time–frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In ...
Institute of Electrical and Electronics Engineers1999
The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictionary of time-frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal-adaptive. In th ...
This report studies the effect of introducing a priori knowledge to recover sparse representations when overcomplete dictionaries are used. We focus mainly on Greedy algorithms and Basis Pursuit as for our algorithmic basement, while a priori is incorporat ...
Compression efficiency is mainly driven by redundancy of the overcomplete set of functions chosen for the signal decomposition. In Matching Pursuit algorithms for example, the redundancy of the dictionary influences the convergence of the residual energy. ...
This correspondence deals with multiwavelets, which are a recent generalization of wavelets in the context of time-varying filter banks and with their applications to signal processing and especially com- pression. By their inherent structure, multiwavelet ...
