Digital image processing using parallel computing based on CUDA technology

Opis bibliograficzny
Parent link:Journal of Physics: Conference Series
Vol. 803 : Information Technologies in Business and Industry (ITBI2016).— 2017.— [012152, 7 p.]
1. autor: Skirnevsky I. P. Igor Petrovich
organizacja autorów: Национальный исследовательский Томский политехнический университет (ТПУ) Институт развития стратегического партнерства и компетенций (ИСПК) Кафедра методики преподавания иностранных языков (МПИЯ), Национальный исследовательский Томский политехнический университет (ТПУ) Институт кибернетики (ИК) Кафедра автоматики и компьютерных систем (АИКС)
Kolejni autorzy: Pustovit A. V., Abdrashitova M. O. Mariya Ovseevna
Streszczenie:Title screen
This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU.
Wydane: 2017
Hasła przedmiotowe:
Dostęp online:http://dx.doi.org/10.1088/1742-6596/803/1/012152
http://earchive.tpu.ru/handle/11683/38195
Format: Elektroniczne Rozdział
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654450
Opis
Streszczenie:Title screen
This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU.
DOI:10.1088/1742-6596/803/1/012152