Share Email Print
cover

Proceedings Paper

Fast edge-preserving image denoising via group coordinate descent on the GPU
Author(s): Madison G. McGaffin; Jeffrey A. Fessler
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We present group coordinate descent algorithms for edge-preserving image denoising that are particularly well-suited to the graphics processing unit (GPU). The algorithms decouple the denoising optimization problem into a set of iterated, independent one-dimensional problems. We provide methods to handle both differentiable regularizers and the absolute value function using the majorize-minimize technique. Specifically, we use quadratic majorizers with Huber curvatures for differentiable potentials and a duality approach for the absolute value function. Preliminary experimental results indicate that the algorithms converge remarkably quickly in time.

Paper Details

Date Published: 7 March 2014
PDF: 9 pages
Proc. SPIE 9020, Computational Imaging XII, 90200P (7 March 2014); doi: 10.1117/12.2042593
Show Author Affiliations
Madison G. McGaffin, Univ. of Michigan (United States)
Jeffrey A. Fessler, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)

© SPIE. Terms of Use
Back to Top