An evaluation of GPU acceleration for sparse reconstruction

Thomas R. Braun

doi:10.1117/12.849536

27 April 2010 An evaluation of GPU acceleration for sparse reconstruction

Thomas R. Braun

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 769715 (2010) https://doi.org/10.1117/12.849536
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Thomas R. Braun "An evaluation of GPU acceleration for sparse reconstruction", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 769715 (27 April 2010); https://doi.org/10.1117/12.849536

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available