A bayesian multi-frame image super-resolution algorithm using the Gaussian Information Filter

Matthew Woods, Aggelos Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Multi-frame image super-resolution (SR) is an image processing technology applicable to any digital, pixilated camera that is limited, by construction, to a certain number of pixels. The objective of SR is to utilize signal processing to overcome the physical limitation and emulate the 'capabilities' of a camera with a higher-density pixel array. SR is well known to be an ill-posed problem and, consequently, state-of-the-art solutions approach it statistically, typically making use of Bayesian inference. Unfortunately, direct marginalization of the posterior distribution resulting from the Bayesian modeling is not analytically tractable. An approximation method, such as Variational Bayesian Inference (VBI), is a powerful tool that retains the advantages of statistical modeling. However, its derivation is tedious and model specific. In this paper, we propose an alternative approximate inference methodology, based upon the well-established, Gaussian Information Filter, which offers a much simpler mathematical derivation while retaining the statistical advantages of VBI.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1368-1372
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Image-Processing
  • Inverse Problems
  • Photogrammetry
  • Remote Sensing
  • Super-Resolution

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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