NDLI: Técnicas de paralelização em GPGPU aplicadas em algoritmo para remoção de ruído multiplicativo

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Técnicas de paralelização em GPGPU aplicadas em algoritmo para remoção de ruído multiplicativo

Content Provider	Semantic Scholar
Author	Gulo, Carlos A. S. J.
Copyright Year	2012
Abstract	Supported by processors evolution, high performance computing have contributed to development in several scientific research areas which require advanced computations, such as image processing, augmented reality, and others. To fully exploit high performance computing available in these resources and to decrease processing time, is necessary apply parallel computing. However, those resources are expensive, which implies the search for alternatives ways to use it. The multicore processors architecture and General Purpose Computing on Graphics Processing Unit (GPGPU) become a low cost options, as they were designed to provide infrastructure for high performance computing and attend real-time applications.With the improvements gained in technologies related to multicomputer, multiprocessor and, more recently, to GPGPUs, the parallelization of computational image processing techniques has gained extraordinary prominence. This parallelization is crucial for the use of such techniques in applications that have strong demands in terms of processing time, so that even more complex computational algorithms can be used, as well as their use on images of higher resolution. In this research, the parallelization in GPGPU of a recent image smoothing method based on a variation model is described and discussed. This method was proposed by Jin and Yang (2011) and is in-demand due to its computation time, and its use with high resolution images. The results obtained are very promising, revealing a speedup about fifteen times in terms of computational speed.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.ibilce.unesp.br/Home/Pos-Graduacao475/CienciadaComputacao/pdf-carlos-alex-sander-juvencio-gulo-m.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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