A GPU parallel implementation of the Local Principal Component Analysis overcomplete method for DW image denoising
Salvatore Cuomo, Pasquale De Michele, Ardelio Galletti, Livia Marcellino · 2016
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.