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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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Document Type: Research Article

Publication date: 29 January 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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