DocumentCode :
3429728
Title :
Resolution enhancement by AdaBoost
Author :
Wu, Junwen ; Trivedi, Mohan ; Rao, Bhaskar
Author_Institution :
CVRR Lab, UC San Diego, La Jolla, CA, USA
Volume :
4
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
893
Abstract :
This work proposes a learning scheme based still image super-resolution reconstruction algorithm. Super-resolution reconstruction is proposed as a binary classification problem and can be solved by conditional class probability estimation. Assuming the probability takes the form of additive logistic regression function, AdaBoost algorithm is used to predict the probability. Experiments on face images validate the algorithm.
Keywords :
image reconstruction; image resolution; probability; regression analysis; AdaBoost algorithm; additive logistic regression function; binary classification problem; conditional class probability estimation; face images; still image super-resolution reconstruction algorithm; Frequency; Image reconstruction; Image resolution; Image storage; Interpolation; Logistics; Probability; Space technology; Statistical learning; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1333916
Filename :
1333916
Link To Document :
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