Sophisticated denoising algorithms are used to improve image quality in the Magnetic Resonance Imaging field. Of course, better results are obtained by implementing computationally expensive schemes. In this paper, we consider the Overcomplete Local Principal Component Analysis (OLPCA) method for image denoising and its main issues. More in detail, we investigated the impact of the Singular Value Decomposition on the OLPCA algorithm and its high computational cost. Moreover, we propose a fine-to-coarse parallelization strategy in order to exploit a parallel hybrid architecture and we implement a multilevel parallel software as a smart combination between codes using NVIDIA cuBLAS library for Graphic Processor Units (GPUs) and the standard Message Passing Interface library for cluster programming. Experimental results show improvements in terms of execution time with a promising speed up with respect to the CPU and our old GPU versions.
|Titolo:||A GPU Implementation of OLPCA Method in Hybrid Environment|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||1.1 Articolo in rivista|