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Manifold Learning for Super Resolution

Type of activity
Theses
Defense's date
2017-02-21
Abstract
The development pace of high-resolution displays has been so fast in the recent years that many images acquired with low-end capture devices are already outdated or will be shortly in time. Super Resolution is central to match the resolution of the already existing image content to that of current and future high resolution displays and applications. This dissertation is focused on learning how to upscale images from the statistics of natural images. We build on a sparsity model that uses learne...
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Pérez Pellitero, Eduardo  (author)
  • Rosenhahn, Bodo  (director)
  • Ruiz Hidalgo, Javier  (director)