We focus on the Overcomplete Local Principal Component Analysis (OLPCA) method, which is widely adopted as denoising filter. We propose a programming approach resorting to Graphic Processor Units (GPUs), in order to massively parallelize some heavy computational tasks of the method. In our approach, we design and implement a parallel version of the OLPCA, by using a suitable mapping of the tasks on a GPU architecture with the aim to investigate the performance and the denoising features of the algorithm. The experimental results show improvements in terms of GFlops and memory throughput.
A GPU parallel implementation of the Local Principal Component Analysis overcomplete method for DW image denoising / Cuomo, Salvatore; DE MICHELE, Pasquale; Galletti, Ardelio; Marcellino, Livia. - 2016-:(2016), pp. 26-31. (Intervento presentato al convegno 2016 IEEE Symposium on Computers and Communication, ISCC 2016 tenutosi a ita nel 2016) [10.1109/ISCC.2016.7543709].
A GPU parallel implementation of the Local Principal Component Analysis overcomplete method for DW image denoising
CUOMO, SALVATORE;DE MICHELE, PASQUALE;GALLETTI, ARDELIO;MARCELLINO, LIVIA
2016
Abstract
We focus on the Overcomplete Local Principal Component Analysis (OLPCA) method, which is widely adopted as denoising filter. We propose a programming approach resorting to Graphic Processor Units (GPUs), in order to massively parallelize some heavy computational tasks of the method. In our approach, we design and implement a parallel version of the OLPCA, by using a suitable mapping of the tasks on a GPU architecture with the aim to investigate the performance and the denoising features of the algorithm. The experimental results show improvements in terms of GFlops and memory throughput.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
