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Sparse Bayesian Learning of Filters for Efficient Image Expansion

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3 Author(s)
Atsunori Kanemura ; Graduate School of Informatics, Department of Electrical Engineering, Kyoto University, University of California, Kyoto, Santa Cruz, JapanCA, USA ; Shin-ichi Maeda ; Shin Ishii

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.

Published in:

IEEE Transactions on Image Processing  (Volume:19 ,  Issue: 6 )