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Proceedings Paper

An evaluation of GPU acceleration for sparse reconstruction
Author(s): Thomas R. Braun
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Paper Abstract

Image processing applications typically parallelize well. This gives a developer interested in data throughput several different implementation options, including multiprocessor machines, general purpose computation on the graphics processor, and custom gate-array designs. Herein, we will investigate these first two options for dictionary learning and sparse reconstruction, specifically focusing on the K-SVD algorithm for dictionary learning and the Batch Orthogonal Matching Pursuit for sparse reconstruction. These methods have been shown to provide state of the art results for image denoising, classification, and object recognition. We'll explore the GPU implementation and show that GPUs are not significantly better or worse than CPUs for this application.

Paper Details

Date Published: 27 April 2010
PDF: 10 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 769715 (27 April 2010); doi: 10.1117/12.849536
Show Author Affiliations
Thomas R. Braun, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)

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