GPU-parallel implementation of the autoregressive model interpolation for high-resolution remote sensing images

Jiaji Wu; Zhan Song; Gwanggil Jeon

doi:10.1117/12.2031463

27 October 2013 GPU-parallel implementation of the autoregressive model interpolation for high-resolution remote sensing images

Jiaji Wu, Zhan Song, Gwanggil Jeon

Author Affiliations +

Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200D (2013) https://doi.org/10.1117/12.2031463
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

The autoregressive modeling image interpolation scheme is noticeably closer to ideal interpolation aiming at obtaining a high-resolution (HR) image from its low-resolution (LR) version than conventional methods. The basic idea is to first estimate the covariance of HR image from the covariance of the LR image and then adjust the covariance coefficients of HR image according to a feedback mechanism that takes into account the mutual influence between the estimated missing pixels in a local window. In spite of its impressive performance, the time-consuming computation is usually the bottleneck of the method when it is applied in time-critical scenario. Graphics Processing Units (GPUs) are attractive candidates to expedite the computation process. In this paper, an efficient GPU-based massively parallel version of the autoregressive modeling image interpolation scheme was proposed. Because all pixels which need to be interpolated have no dependence, each estimated pixel is assigned to independent thread in our parallel interpolation scheme. Experimental results show that we reached a speedup of 21.2x when I/O transfer time was taken into account, with respect to the original single-threaded C CPU code with the -O2 compiling optimization.

Citation Download Citation

Jiaji Wu, Zhan Song, and Gwanggil Jeon "GPU-parallel implementation of the autoregressive model interpolation for high-resolution remote sensing images", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200D (27 October 2013); https://doi.org/10.1117/12.2031463

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Autoregressive models

Image interpolation

Lawrencium

Image quality

Remote sensing

Image processing

Visualization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years