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Proceedings Paper

Fast edge-preserving image denoising via group coordinate descent on the GPU
Author(s): Madison G. McGaffin; Jeffrey A. Fessler
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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)

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