Peer group and fuzzy metric to remove noise in images using heterogeneous computing

Authors:
Ma. Guadalupe Sánchez;Vicente Vidal;Jordi Bataller
Affiliations:
Departamento de Sistemas y Computación, Instituto Tecnológico de Cd. Guzmán, Cd. Guzmán, Jalisco, Mexico;Departamento de Sistemas Informáticos y Computación E.P.S. Gandia, Universidad Politécnica de Valencia, Grao de Gandia, Valencia, Spain;Departamento de Sistemas Informáticos y Computación E.P.S. Gandia, Universidad Politécnica de Valencia, Grao de Gandia, Valencia, Spain
Venue:
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Year:
2011

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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 multi-core and multi-GPU a greater number of Mpixels/second are processed.