Neighbor combination for atmospheric turbulence image reconstruction

Dong Gong, Yanning Zhang, Shaobo Dang, Jinqiu Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, we propose a novel neighbor combination framework for the reconstruction of the atmospheric turbulence degenerated image sequence. To utilize the spatial and temporal redundancy, a neighbor vector sampling strategy in spatial and temporal domain is conducted relying on the modeling of the registered sequence. Then, a combinator of neighbor vectors is developed based on a resampling maximum likelihood model and a relative approximation. Relying on the neighbor combination and spatial-invariant deconvolution, a clear image is reconstructed. Experiments on real data sets demonstrate the effectiveness of this framework.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages1361-1365
Number of pages5
ISBN (Print)9781479923410
DOIs
StatePublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • atmospheric turbulence
  • image patch detection
  • Image reconstruction
  • image sequence analysis
  • maximum likelihood estimation

Fingerprint

Dive into the research topics of 'Neighbor combination for atmospheric turbulence image reconstruction'. Together they form a unique fingerprint.

Cite this