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Super-Resolution from a Single Image
| Citations: | 135 - 5 self |
BibTeX
@MISC{Glasner_super-resolutionfrom,
author = {Daniel Glasner and Shai Bagon and Michal Irani},
title = {Super-Resolution from a Single Image},
year = {}
}
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Abstract
Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales. 1.
Keyphrases
single image different scale example-based super-resolution subpixel misalignment classical multi-image super-resolution possible resolution increase image scale whereas recurrence approach attempt high resolution image patch super resolution combined approach prior example many time unified framework natural image tend patch redundancy classical super-resolution
