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Department of Computer Science

Accelerated deconvolution of radio interferometric images using orthogonal matching pursuit and graphics hardware

Abstract

dc:description.abstract

Deconvolution of native radio interferometric images constitutes a major computational component of the radio astronomy imaging process. An efficient and robust deconvolution operation is essential for reconstruction of the true sky signal from measured correlator data. Traditionally, radio astronomers have mostly used the CLEAN algorithm, and variants thereof. However, the techniques of compressed sensing provide a mathematically rigorous framework within which deconvolution of radio interferometric images can be implemented. We present an accelerated implementation of the orthogonal matching pursuit (OMP) algorithm (a compressed sensing method) that makes use of graphics processing unit (GPU) hardware, and show significant accuracy improvements over the standard CLEAN. In particular, we show that OMP correctly identifies more sources than CLEAN, identifying up to 82% of the sources in 100 test images, while CLEAN only identifies up to 61% of the sources. In addition, the residual after source extraction is 2.7 times lower for OMP than for CLEAN. Furthermore, the GPU implementation of OMP performs around 23 times faster than a 4-core CPU.

Degree

thesis:*
Grantor dc:publisher.institution
Department of Computer Science
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Van Belle, Jonathan
Advisors dc:contributor.advisor
  • Gain, James E
  • Armstrong, Richard

Rights

Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/11427/22991
OAI identifier oai:identifier
oai:open.uct.ac.za:11427/22991

Chain of custody

source
Harvested from
University of Cape Town
Base URL
open.uct.ac.za/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Van Belle, Jonathan. Accelerated deconvolution of radio interferometric images using orthogonal matching pursuit and graphics hardware. Department of Computer Science, 2016. http://hdl.handle.net/11427/22991