Loading...
Loading...

Go to the content (press return)

Bayesian region selection for adaptive dictionary-based Super-Resolution

Author
Pérez-Pellitero, E.; Salvador, J.; Ruiz-Hidalgo, J.; Rosenhahn, B.
Type of activity
Presentation of work at congresses
Name of edition
24th British Machine Vision Conference
Date of publication
2013
Presentation's date
2013-09-30
Book of congress proceedings
BMVC 2013: Proceedings of the 13th British Machine Vision Conference: 9-13 September 2013, Bristol University, UK
First page
1
Last page
11
Repository
http://hdl.handle.net/2117/21666 Open in new window
URL
http://www.tnt.uni-hannover.de/papers/data/988/PerezPellitero2013Bmvc.pdf Open in new window
Abstract
The performance of dictionary-based super-resolution (SR) strongly depends on the contents of the training dataset. Nevertheless, many dictionary-based SR methods randomly select patches from of a larger set of training images to build their dictionaries [ 8 , 14 , 19 , 20 ], thus relying on patches being diverse enough. This paper describes a dictionary building method for SR based on adaptively selecting an optimal subset of patches out of the training images. Each training image is divided in...
Citation
Pérez-Pellitero, E. [et al.]. Bayesian region selection for adaptive dictionary-based Super-Resolution. A: British Machine Vision Conference. "BMVC 2013: Proceedings of the 13th British Machine Vision Conference: 9-13 September 2013, Bristol University, UK". Bristol: 2013, p. 1-11.
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Pérez Pellitero, Eduardo  (author and speaker )
  • Salvador, Jordi  (author and speaker )
  • Ruiz Hidalgo, Javier  (author and speaker )
  • Rosenhahn, Bodo  (author and speaker )

Attachments