Abstract
In this paper, we report a study on the parallelization of an algorithm
for removing impulsive noise in images. The algorithm is based on the concept
of peer group and fuzzy metric. We have developed implementations using
Open Multi-Processing (OpenMP) and Compute Unified Device Architecture
(CUDA) for Graphics Processing Unit (GPU). Many sequential algorithms have
been proposed to remove noise, but their computational cost is excessive for
real-time processing of large images. We developed implementations for a multi-
core CPU, for a multi-GPU (several GPUs) and for a combination of both.
These implementations were compared also with different sizes of the image in
order to find out the settings with the best performance. A study is made using
the shared memory and texture memory to minimize access time to data in GPU
global memory. The result shows that when the image is distributed in multicore
and multi-GPU a greater number of Mpixels/second are processed.
