3D data denoising via Non-Local means filter by using parallel GPU strategies

Salvatore Cuomo, Pasquale De Michele, Francesco Piccialli
Department of Mathematics and Applications "R. Caccioppoli", University of Naples "Federico II"
Computational and Mathematical Methods in Medicine, 2014


   title={3D data denoising via Non-Local means filter by using parallel GPU strategies},

   author={Cuomo, Salvatore and De Michele, Pasquale and Piccialli, Francesco},



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Non-Local 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 achieve reasonable running times by filtering, slice-by-slice, 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D Non-Local 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.
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