Gauss-Markov Random field model for non-quadratic regularization of complex SAR images

  • Authors:

  • Dušan Gleich;Peter Planinšič;Matej Kseneman;Matteo Soccorsi

  • Affiliations:

  • University of Maribor, Faculty of Electrical Engineering, Maribor, Slovenia;University of Maribor, Faculty of Electrical Engineering, Maribor, Slovenia;University of Maribor, Faculty of Electrical Engineering, Maribor, Slovenia;German Aerospace Center, Remote Sensing Technology Institute, Weßling, Germany

  • Venue:
  • ICOSSSE'08 Proceedings of the 7th WSEAS international conference on System science and simulation in engineering
  • Year:
  • 2008

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Abstract

This paper presents despeckling and information extraction using non-quadratic regularization. The novelty of this paper is that instead of the Gaussian prior model a Gauss-Markov random field model is chosen, because it can efficiently model textures in the images. The iterative procedure consist of noise-free image and texture parameter. The experimental results show that the proposed method satisfactorily removes noise form synthetic and real SAR images and is comparable with the state of the art methods using objective measurements on synthetic SAR images.