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Title:Subsampled Multichannel Blind Deconvolution by Sparse Power Factorization
Author(s):Lee, Kiryung; Yarkony, Elad; Bresler, Yoram
Subject(s):Multichannel blind deconvolution
pMRI
Superresolution
Alternating minimization
Sparse model
Abstract:In this technical report, we show that sparse power factorization (SPF) is an effective solution to the subsampled multichannel blind deconvolution (SMBD) problem when the input signal follows a sparse model. SMBD is formulated as the recovery of a sparse rank-one matrix. Unlike the recovery of rank-one matrix or of sparse matrix, when there are multiple priors on the solution simultaneously, SPF outperforms convex relaxation approaches both theoretically and empirically. We confirm that SPF exhibits the same advantage in the context of SMBD.
Issue Date:2013-09
Publisher:Coordinated Science Laboratory. University of Illinois at Urbana-Champaign.
Series/Report:Coordinated Science Laboratory Report no. UILU-ENG-13-2207
Genre:Technical Report
Type:Text
Language:English
URI:http://hdl.handle.net/2142/90440
Sponsor:National Science Foundation/CCF 10-18789
Date Available in IDEALS:2016-07-07


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