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Bayesian region selection for adaptive dictionary-based Super-Resolution

Autor
Pérez-Pellitero, E.; Salvador, J.; Ruiz-Hidalgo, J.; Rosenhahn, B.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
24th British Machine Vision Conference
Any de l'edició
2013
Data de presentació
2013-09-30
Llibre d'actes
BMVC 2013: Proceedings of the 13th British Machine Vision Conference: 9-13 September 2013, Bristol University, UK
Pàgina inicial
1
Pàgina final
11
Repositori
http://hdl.handle.net/2117/21666 Obrir en finestra nova
URL
http://www.tnt.uni-hannover.de/papers/data/988/PerezPellitero2013Bmvc.pdf Obrir en finestra nova
Resum
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...
Citació
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.
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Pérez Pellitero, Eduardo  (autor ponent)
  • Salvador, Jordi  (autor ponent)
  • Ruiz Hidalgo, Javier  (autor ponent)
  • Rosenhahn, Bodo  (autor ponent)

Arxius