Alternating Minimization Algorithm for Speckle Reduction With a Shifting Technique

Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmen...

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Veröffentlicht in:IEEE transactions on image processing Jg. 21; H. 4; S. 1701 - 1714
Hauptverfasser: Woo, Hyenkyun, Yun, Sangwoon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.04.2012
Schlagworte:
Algorithms
Alternating minimization
Computational modeling
Computer Simulation
convex optimization
denoising
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Lagrangian functions
Laplace equations
Minimization
Models, Statistical
multiplicative noise
Numerical models
Reproducibility of Results
Sensitivity and Specificity
Speckle
synthetic aperture radar (SAR)
total variation (TV)
Vectors
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmented Lagrangian function have been proposed to efficiently solve speckle-reduction variational models with TV regularization. However, these algorithms require inner iterations or inverses involving the Laplacian operator at each iteration. In this paper, we adapt Tseng's alternating minimization algorithm with a shifting technique to efficiently remove the speckle without any inner iterations or inverses involving the Laplacian operator. The proposed method is very simple and highly parallelizable; therefore, it is very efficient to despeckle huge-size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle-reduction variational models with a TV regularizer in terms of central-processing-unit time.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2011.2176345