A MAP estimation based segmentation model for speckled images

Yu Han, George Baciu, Chen Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a new fuzzy-based variational model that efficiently computes partitioning of speckled images, such as images obtained from Synthetic Aperture Radar (SAR). The model is derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are: (1) the Gamma distribution rather than the classical Gaussian distribution is used to model the gray intensities in each homogeneous region of the images (Gamma distribution function is better suited for speckled images); (2) an adaptive weighted regularization term with respect to a fuzzy membership function is designed to protect the segmentation results from degeneration (being over-smoothed). Compared with the classical total variation (TV) regularizer, the proposed regularization term has a sparser property. In addition, a new alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and the Chambolle's projection method. Numerical examples are given to verify the efficiency of our model.
Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Smart Computing, SMARTCOMP 2014
PublisherIEEE
Pages35-41
Number of pages7
ISBN (Electronic)9781479957118
DOIs
Publication statusPublished - 17 Feb 2014
Event2014 1st International Conference on Smart Computing, SMARTCOMP 2014 - Hong Kong, Hong Kong
Duration: 3 Nov 20145 Nov 2014

Conference

Conference2014 1st International Conference on Smart Computing, SMARTCOMP 2014
Country/TerritoryHong Kong
CityHong Kong
Period3/11/145/11/14

Keywords

  • alternative direction iteration
  • Chambolle's projection
  • MAP
  • Speckled image
  • split Bregman

ASJC Scopus subject areas

  • Computer Science(all)

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