Vector anisotropic filter for multispectral image denoising

Ahmed Ben Said, Sebti Foufou, Rachid Hadjidj

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

Abstract

In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.

Original languageEnglish (US)
Title of host publicationTwelfth International Conference on Quality Control by Artificial Vision
EditorsFabrice Meriaudeau, Olivier Aubreton
PublisherSPIE
ISBN (Electronic)9781628416992
DOIs
StatePublished - 2015
Event12th International Conference on Quality Control by Artificial Vision - Le Creusot, France
Duration: Jun 3 2015Jun 5 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9534
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other12th International Conference on Quality Control by Artificial Vision
CountryFrance
CityLe Creusot
Period6/3/156/5/15

Keywords

  • Anisotropic filter
  • Multispectral image
  • Sparse matrix transform

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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