Sparse Bayesian learning of filters for efficient image expansion

Authors:
Atsunori Kanemura;Shin-ichi Maeda;Shin Ishii
Affiliations:
ATR Neural Information Analysis Laboratories, Kyoto, Japan and Graduate School of Informatics, Kyoto University, Kyoto, Japan and Department of Electrical Engineering, University of California, Sa ...;Graduate School of Informatics, Kyoto University, Kyoto, Japan;Graduate School of Informatics, Kyoto University, Kyoto, Japan
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.