Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43926
Title: A variational based smart segmentation model for speckled images
Authors: Han, Y
Xu, C
Baciu, G 
Keywords: Alternative direction iteration
Chambolle's projection
MAP
Speckled image
Split bregman
Issue Date: 2016
Publisher: Elsevier
Source: Neurocomputing, 2016, v. 178, p. 62-70 How to cite?
Journal: Neurocomputing 
Abstract: In this paper, we propose a new variational model in the fuzzy framework to achieve the task of partitioning speckled images, such as the synthetic aperture radar (SAR) images. The model is partly derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are that (1) the Gamma distribution rather than the classical Gaussian distribution is used to simulate the gray intensities in each homogeneous region of the images; (2) a smart regularization term with respect to fuzzy membership functions is designed. The newly designed regularization term equals to an adaptive weighted total variation (TV) regularizer. Compared with the classical TV regularizer, the proposed regularization term not only has a sparser property, but also protects the segmentation results from degeneration (being over-smoothed). In addition, a new algorithm based on the alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and Chambolle's projection method. Numerical examples are given to verify the promising efficiency of our model.
URI: http://hdl.handle.net/10397/43926
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2015.07.115
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