University of Illinois Urbana-Champaign

Subsampled Multichannel Blind Deconvolution by Sparse Power Factorization

Lee, Kiryung; Yarkony, Elad; Bresler, Yoram

Content Files
UILU-ENG-13-2207.pdf

Permalink

https://hdl.handle.net/2142/90440

Description

Title
Subsampled Multichannel Blind Deconvolution by Sparse Power Factorization
Author(s)
  • Lee, Kiryung
  • Yarkony, Elad
  • Bresler, Yoram
Issue Date
2013-09
Keyword(s)
  • Multichannel blind deconvolution
  • pMRI
  • Superresolution
  • Alternating minimization
  • Sparse model
Date of Ingest
  • 2016-07-07T14:37:21Z
  • 2017-07-14T23:13:04Z
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.
Publisher
Coordinated Science Laboratory, University of Illinois at Urbana-Champaign
Series/Report Name or Number
Coordinated Science Laboratory Report no. UILU-ENG-13-2207
Type of Resource
text
Genre of Resource
Technical Report
Language
en
Permalink
http://hdl.handle.net/2142/90440
Sponsor(s)/Grant Number(s)
National Science Foundation/CCF 10-18789

Owning Collections

Report - Coordinated Science Laboratory PRIMARY