Francisco Peña-Cantillana, D. Díaz-Pernil, H. Christinal, M. A. Gutiérrez-Naranjo
{"title":"Implementation on CUDA of the Smoothing Problem with Tissue-Like P Systems","authors":"Francisco Peña-Cantillana, D. Díaz-Pernil, H. Christinal, M. A. Gutiérrez-Naranjo","doi":"10.4018/jncr.2011070103","DOIUrl":null,"url":null,"abstract":"Smoothing is often used in Digital Imagery for improving the quality of an image by reducing its level of noise. This paper presents a parallel implementation of an algorithm for smoothing 2D images in the framework of Membrane Computing. The chosen formal framework has been tissue-like P systems. The algorithm has been implemented by using a novel device architecture called CUDA (Compute Unified Device Architecture) which allows the parallel NVIDIA Graphics Processors Units (GPUs) to solve many complex computational problems. Some examples are presented and compared; research lines for the future are also discussed.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Nat. Comput. Res.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/jncr.2011070103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
Smoothing is often used in Digital Imagery for improving the quality of an image by reducing its level of noise. This paper presents a parallel implementation of an algorithm for smoothing 2D images in the framework of Membrane Computing. The chosen formal framework has been tissue-like P systems. The algorithm has been implemented by using a novel device architecture called CUDA (Compute Unified Device Architecture) which allows the parallel NVIDIA Graphics Processors Units (GPUs) to solve many complex computational problems. Some examples are presented and compared; research lines for the future are also discussed.
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