Enhanced ultrasound image reconstruction using a compressive blind deconvolution approach

Zhouye Chen, Adrian Basarab, Denis Kouamé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Compressive deconvolution, combining compressive sampling and image deconvolution, represents an interesting possibility to reconstruct enhanced ultrasound images from compressed measurements. The model of compressive deconvolution includes, in addition to the measurement matrix, a 2D convolution operator carrying the information on the system point spread function which is usually unkown in practice. In this paper, we propose a novel alternating minimization-based optimization scheme to invert the resulting linear model, to jointly reconstruct enhanced ultrasound images and estimate the point spread function. The performance of the method is evaluated on both Shepp-Logan phantom and simulated ultrasound data.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages6245-6249
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
ISSN (Print)2379-190X

Conference

Conference42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated titleICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Alternating minimization
  • Blind deconvolution
  • Compressive sampling
  • Ultrasound imaging

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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