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7 March 2014 Fast edge-preserving image denoising via group coordinate descent on the GPU
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Proceedings Volume 9020, Computational Imaging XII; 90200P (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
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.
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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);

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