publication . Conference object . Other literature type . 2009

Super-resolution from a single image

Glasner, Daniel; Bagon, Shai; Irani, Michal;
Open Access
  • Published: 01 Sep 2009
  • Publisher: IEEE
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 time...
Subjects
free text keywords: Computer science, Computer vision, Superresolution, Image scale, Image resolution, Pattern recognition, Redundancy (engineering), Super resolution algorithm, Kernel (linear algebra), Artificial intelligence, business.industry, business, Subpixel rendering, Pixel
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publication . Conference object . Other literature type . 2009

Super-resolution from a single image

Glasner, Daniel; Bagon, Shai; Irani, Michal;