Chao-Tsung Chu

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Learning-based approaches for super-resolution (SR) have been studied in the past few years. In this paper, a novel single-image SR framework based on the learning of sparse image representation with support vector regression (SVR) is presented. SVR is known to offer excellent generalization ability in predicting output class labels for input data. Given a(More)
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