Robust super-resolution image reconstruction method for geometrically deformed remote sensing images

Jing Qin, Igor Yanovsky

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

5 Scopus citations

Abstract

Due to the limitations of imaging sensors, remote sensing images often have limited resolution. To address this issue, various super-resolution (SR) image reconstruction techniques have been developed to reconstruct a high-resolution image from a sequence of low-resolution, noisy and blurry observations. In this paper, we propose an efficient super-resolution image reconstruction method for geometrically deformed remote sensing images, based on the nonlocal total variation (NLTV) regularization. The proposed minimization problem is solved by a fast primal-dual algorithm. Numerical experiments demonstrate the performance of the proposed method.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Pages8050-8053
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - Oct 31 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: Jul 22 2018Jul 27 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period7/22/187/27/18

Bibliographical note

Funding Information:
The authors would like to thank Stamatis Lefkimmiatis from Skolkovo Institute of Science and Technology for providing advice on efficiently implementing the NLTV regularization.

Publisher Copyright:
© 2018 IEEE

Keywords

  • Primal-dual algorithm
  • Remote sensing images
  • Super-resolution image reconstruction

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences (all)

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