Fast edge-preserving image denoising via group coordinate descent on the GPU

Madison G. McGaffin; Jeffrey A. Fessler

doi:10.1117/12.2042593

7 March 2014 Fast edge-preserving image denoising via group coordinate descent on the GPU

Madison G. McGaffin, Jeffrey A. Fessler

Proceedings Volume 9020, Computational Imaging XII; 90200P (2014) https://doi.org/10.1117/12.2042593
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

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.

Citation Download Citation

Madison G. McGaffin and Jeffrey A. Fessler "Fast edge-preserving image denoising via group coordinate descent on the GPU", Proc. SPIE 9020, Computational Imaging XII, 90200P (7 March 2014); https://doi.org/10.1117/12.2042593

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