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NIPS
2003

A Sampled Texture Prior for Image Super-Resolution

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A Sampled Texture Prior for Image Super-Resolution
Super-resolution aims to produce a high-resolution image from a set of one or more low-resolution images by recovering or inventing plausible high-frequency image content. Typical approaches try to reconstruct a high-resolution image using the sub-pixel displacements of several lowresolution images, usually regularized by a generic smoothness prior over the high-resolution image space. Other methods use training data to learn low-to-high-resolution matches, and have been highly successful even in the single-input-image case. Here we present a domain-specific image prior in the form of a p.d.f. based upon sampled images, and show that for certain types of super-resolution problems, this sample-based prior gives a significant improvement over other common multiple-image super-resolution techniques.
Lyndsey C. Pickup, Stephen J. Roberts, Andrew Ziss
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Lyndsey C. Pickup, Stephen J. Roberts, Andrew Zisserman
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