Abstract
We propose a novel high-quality image zooming technique using fuzzy rule based prediction framework. Our approach is based on the fundamental idea that a low-resolution (LR) image patch could be generated from any of the many possible high-resolution (HR) image patches. Therefore it would be natural to assign certain fuzzyness to each of the possibilities of HR patches. We develop a prediction system that learns the LR-HR patch correspondence and also the natural image patch prior from an external database. We do so by collecting a large amount of the LR-HR natural image patch pairs from an existing database, grouping them into different clusters and then generating fuzzy rules to get an efficient mapping from LR patch space to HR patch space. Experimental results show the efficacy of our method over existing state-of-art methods.
Published Version
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