3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies. (15th June 2014)

• Record Type: Journal Article
• Authors: Cuomo, Salvatore; De Michele, Pasquale; Piccialli, Francesco
• Other Names: Klebanov Lev Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2014(2014)
• Publication Date: 2014-06-15
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2014/523862
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.