NON-LOCAL MEANS LOW-RANK APPROXIMATION FOR HYPERSPECTRAL DENOISING

Bin Zhao, Johannes R. Sveinsson, Magnus O. Ulfarsson, Jocelyn Chanussot

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents a non-local means low-rank approximation (NLMLRA) denoising method for hyperspectral images (HSIs). NLMLRA uses a Slanted Butterworth function to construct a low-rank approximation for non-local means (NLM) operator and is efficiently implemented based on Chebyshev polynomials. The proposed method is evaluated by using both simulated and real hyperspectral datasets.

Original languageEnglish
Pages4147-4150
Number of pages4
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Bibliographical note

Funding Information:
This work was supported in part by the Doctoral Grants of the University of Iceland Research Fund and the Icelandic Research Fund under Grant 174075-05.

Publisher Copyright:
©2021 IEEE

Other keywords

  • Denoising
  • hyperspectral image
  • low-rank approximation
  • non-local means

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