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Efficient parallelization on GPU of an image smoothing method based on a variational model

Title
Efficient parallelization on GPU of an image smoothing method based on a variational model
Type
Article in International Scientific Journal
Year
2019-08
Authors
Carlos A. S. J. Gulo
(Author)
Other
Henrique F. de Arruda
(Author)
Other
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Alex F. de Araujo
(Author)
Other
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Antonio C. Sementille
(Author)
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João Manuel R. S. Tavares
(Author)
FEUP
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Journal
Vol. 16 No. 4
Pages: 1249-1261
ISSN: 1861-8200
Publisher: Springer Nature
Indexing
Scientific classification
CORDIS: Technological sciences
FOS: Engineering and technology
Other information
Authenticus ID: P-00K-P26
Resumo (PT):
Abstract (EN): Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 13
Documents
File name Description Size
10.1007_s11554-016-0623-x 1st Page 1693.22 KB
RTIP-D-14-00091 Paper Draft 1333.00 KB
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