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Volume: 29 | Article ID: art00014
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Single Image Super-Interpolation using Adjusted Self-Exemplars
  DOI :  10.2352/ISSN.2470-1173.2017.17.COIMG-429  Published OnlineJanuary 2017
Abstract

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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Hyun-Ho Kim, Jae-Seok Choi, Munchurl Kim, "Single Image Super-Interpolation using Adjusted Self-Exemplarsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XV,  2017,  pp 81 - 86,  https://doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-429

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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