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  1. Proceedings of the 9th European Conference on Visual Media Production (CVMP '12)
  2. Self-content super-resolution for ultra-HD up-sampling
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Self-content super-resolution for ultra-HD up-sampling

Content Provider ACM Digital Library
Author Guillotel, Philippe Türkan, Mehmet Thoreau, Dominique
Abstract We describe a self-content single image super-resolution algorithm based on multi-scale neighbor embeddings of image patches. We make use of the recurrence property of similar patches across different scales of an image. Inspired by manifold learning approaches, we first characterize the local geometry of a given low-resolution patch by reconstructing it from similar patches taken from down-scaled versions of the input image. We then hallucinate the high-resolution patch by relying on local geometric similarities of low- and high-resolution patch spaces. We enforce the local compatibility through patch overlapping, and preserve the structures with a sparsity-based patch averaging. We further enforce the global consistency through back-projection. Noting that this method uses as little as the self-information contained in a given low-resolution image and its implementation is well suited to GPU processors thanks to highly parallelization, our experimental results demonstrate similar or even better performance with respect to state-of-the-art superresolution methods.
Starting Page 49
Ending Page 58
Page Count 10
File Format PDF
ISBN 9781450313117
DOI 10.1145/2414688.2414695
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-12-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Multi-scale neighbor embedding Self-content super-resolution Ultra-hd up-sampling Locally linear embedding
Content Type Text
Resource Type Article
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