DocumentCode
2072644
Title
Comparison of Super-Resolution Algorithms Using Image Quality Measures
Author
Begin, Isabelle ; Ferrie, Frank P.
Author_Institution
McGill University, Canada
fYear
2006
fDate
07-09 June 2006
Firstpage
72
Lastpage
72
Abstract
This paper presents comparisons of two learning-based super-resolution algorithms as well as standard interpolation methods. To allow quality assessment of results, a comparison of a variety of image quality measures is also performed. Results show that a MRF-based super-resolution algorithm improves a previously interpolated image. The estimated degree of improvement varies both according to the quality measure chosen for the comparison as well as the image class.
Keywords
Algorithm design and analysis; Application software; Bayesian methods; Computer vision; Image edge detection; Image quality; Image resolution; Interpolation; Performance evaluation; Quality assessment;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision, 2006. The 3rd Canadian Conference on
Print_ISBN
0-7695-2542-3
Type
conf
DOI
10.1109/CRV.2006.23
Filename
1640427
Link To Document