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Open Access Single Image Super-Interpolation using Adjusted Self-Exemplars

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Super-resolution (SR) is an elegant technique that can reconstruct high-resolution (HR) videos/images from their lowresolution (LR) counterparts. Most of the conventional SR methods utilize linear mappings to learn complex LR-to-HR relationships, where these linear mappings are often learned from training. Inspired by our previous linear mapping based SR method [1], we propose a novel super-interpolation based SR method that utilizes adjusted self-exemplars. That is, in order to find sufficient amounts of LR-HR patch pairs in self-exemplars, we iteratively augment selfexemplars from an LR input image to create additional selfexemplars. In doing so, our proposed SR method is able to find welllearned linear mappings on-line from self-exemplars without using external training images, and outperforms other conventional SR methods.

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Keywords: ADJUSTED SELF-EXEMPLAR; ENSEMBLE; LINEAR MAPPING; SUPER-INTERPOLATION; SUPER-RESOLUTION

Document Type: Research Article

Publication date: 29 January 2017

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