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GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring.

Authors :
Fiandrotti, Attilio
Fosson, Sophie M
Ravazzi, Chiara
Magli, Enrico
Source :
International Journal of Remote Sensing. Apr2018, Vol. 39 Issue 7, p2043-2065. 23p.
Publication Year :
2018

Abstract

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting graphical processing unit (GPU)’s parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a 10-fold signal recovery speedup, thanks to ad-hoc parallelization of matrix–vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
01431161
Volume :
39
Issue :
7
Database :
Academic Search Index
Journal :
International Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
127266187
Full Text :
https://doi.org/10.1080/01431161.2017.1356489