Real-time image deconvolution on the GPU

James T. Klosowski; Shankar Krishnan

doi:10.1117/12.872152

25 January 2011 Real-time image deconvolution on the GPU

James T. Klosowski, Shankar Krishnan

Proceedings Volume 7872, Parallel Processing for Imaging Applications; 78720H (2011) https://doi.org/10.1117/12.872152
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Two-dimensional image deconvolution is an important and well-studied problem with applications to image deblurring and restoration. Most of the best deconvolution algorithms use natural image statistics that act as priors to regularize the problem. Recently, Krishnan and Fergus provide a fast deconvolution algorithm that yields results comparable to the current state of the art. They use a hyper-Laplacian image prior to regularize the problem. The resulting optimization problem is solved using alternating minimization in conjunction with a half-quadratic penalty function. In this paper, we provide an efficient CUDA implementation of their algorithm on the GPU. Our implementation leverages many wellknown CUDA optimization techniques, as well as several others that have a significant impact on this particular algorithm. We discuss each of these, as well as make a few observations regarding the CUFFT library. Our experiments were run on an Nvidia GeForce GTX 260. For a single channel image of size 710 x 470, we obtain over 40 fps, while on a larger image of size 1900 x 1266, we get almost 6 fps (without counting disk I/O). In addition to linear performance, we believe ours is the first implementation to perform deconvolutions at video rates. Our running times also demonstrate that our GPU implementation is over 27 times faster than the original CPU implementation.

Citation Download Citation

James T. Klosowski and Shankar Krishnan "Real-time image deconvolution on the GPU", Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720H (25 January 2011); https://doi.org/10.1117/12.872152

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