Open AccessRecovery of Missing Samples with Sparse Approximations
Author(s) -
Benjamin G. Salomon
Publication year - 2013
Publication title -
isrn signal processing
Language(s) - English
Resource type - Journals
eISSN - 2090-505X
pISSN - 2090-5041
DOI - 10.1155/2013/830723
Subject(s) - matching pursuit , bandlimiting , missing data , basis (linear algebra) , mathematics , algorithm , matching (statistics) , unitary state , discrete fourier transform (general) , orthogonal basis , basis function , fourier transform , basis pursuit , computer science , fractional fourier transform , mathematical analysis , fourier analysis , statistics , compressed sensing , geometry , physics , quantum mechanics , political science , law
In most missing samples problems, the signals are assumed to be bandlimited. That is, the signals are assumed to be sparsely approximated by a known subset of the discrete Fourier transform basis vectors. We discuss the recovery of missing samples when the signals can be sparsely approximated by an unknown subset of certain unitary basis vectors. We propose the use of the orthogonal matching pursuit to recover missing samples by sparse approximations.
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