Subsampled Multichannel Blind Deconvolution by Sparse Power Factorization

Lee, Kiryung; Yarkony, Elad; Bresler, Yoram

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

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