Variational bayesian super-resolution reconstruction

S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

In many imaging applications, acquiring an image of a scene with high spatial resolution is not possible due to a number of theoretical and practical limitations. These limitations include for instance the sensor resolution, the Rayleigh resolution limit, the increased cost, data transfer rate, and the amount of shot noise due to the size of the digital sensor, among others. In these cases, super-resolution (SR) methods can be utilized to process one or more low-resolution (LR) images of the scene together to obtain a high-resolution (HR) image. The basic principle of super-resolution is that changes in the LR images caused by the blur and the (camera or scene) motion provide additional data that can be utilized to reconstruct the HR image from the set of LR observations. Super-resolution methods are widely utilized in a number of imaging fields, such as surveillance, remote sensing, medical and nano-imaging.

Original languageEnglish (US)
Title of host publicationSuper-Resolution Imaging
PublisherCRC Press
Pages285-314
Number of pages30
ISBN (Electronic)9781439819319
ISBN (Print)9781439819302
DOIs
StatePublished - Jan 1 2017

ASJC Scopus subject areas

  • Computer Science(all)
  • Physics and Astronomy(all)
  • Engineering(all)

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