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Alternating minimization algorithm for shifted speckle reduction variational model

Title
Alternating minimization algorithm for shifted speckle reduction variational model
Authors
Yun S.Woo H.
Ewha Authors
우현균
Issue Date
2011
Journal Title
International Geoscience and Remote Sensing Symposium (IGARSS)
Indexed
SCOPUS scopus
Abstract
In synthetic aperture radar (SAR), the observed image is corrupted by the speckle (multiplicative noise). The variational models with the total variation (TV) regularization have attracted much interest in reducing the speckle due to the edge preserving feature of TV. Recently, several TV regularized convex variational models, such as the maximum a posteriori (MAP) model for a log-transformed image and the I-divergence model, have been proposed. In this paper, we adapt Tseng's alternating minimization algorithm to solve the proposed shifted speckle reduction variational models with TV. The algorithm for the proposed shifted variational models does not require any inner iteration or inversion involving the Laplacian operator that is required in recent algorithms based on an augmented Lagrangian framework. Hence, the proposed method is very simple and highly parallelizable and so efficient to despeckle huge SAR images. © 2011 IEEE.
DOI
10.1109/IGARSS.2011.6050050
ISBN
9781457710056
Appears in Collections:
연구기관 > 수리과학연구소 > Journal papers
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