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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 523862, 14 pages
Research Article

3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies

Department of Mathematics and Applications “R. Caccioppoli”, University of Naples “Federico II”, Via Cintia, 80126 Napoli, Italy

Received 4 March 2014; Revised 2 May 2014; Accepted 2 May 2014; Published 16 June 2014

Academic Editor: Lev Klebanov

Copyright © 2014 Salvatore Cuomo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.