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.

Date Published: 7 March 2014
PDF: 9 pages
Proc. SPIE 9020, Computational Imaging XII, 90200P (7 March 2014); doi: 10.1117/12.2042593

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