Ming-Chun Yang

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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)
This paper presents a novel learning-based method for single image super-resolution (SR). Given an input low-resolution image and its image pyramid, we propose to perform context-constrained image segmentation and construct an image segment dataset with different context categories. By learning context-specific image sparse representation, our method aims(More)
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