Name
Abstract | ||
|---|---|---|
This paper introduces a novel Bayesian method for speckle suppression of SAR images. We first analyze the logarithmic transform of the original image by means of the curvelet transform that handles image edges more efficiently than wavelet transform. In a recent work [1], we have shown that due to the statistical properties of the curvelet subbands of SAR images, they can be modelled by two-dimensional Generalized Autoregressive Conditional Heteroscedastic (2D-GARCH) model. Here, we employ a generalization of 2D-GARCH model, called 2D-GARCH Generalized Gaussian (2D-GARCH-GG), to these coefficients. This model preserves the appropriate properties of 2D-GARCH for modeling the curvelet coefficients while extends the dynamic formulation of 2D-GARCH model. Consequently, we design a maximum a-posteriori (MAP) estimator for estimating the clean image curvelet coefficients. Finally, we compare our proposed method with other denoising methods, and quantify the achieved performance improvement. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288114 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
Bayes methods,Gaussian distribution,curvelet transforms,image denoising,radar imaging,speckle,synthetic aperture radar,2D-GARCH generalized Gaussian model,Bayesian method,SAR images,curvelet domain speckle suppression,curvelet subbands,image denoising,logarithmic transform,maximum a-posteriori estimator,statistical properties,two-dimensional generalized autoregressive conditional heteroscedastic model,2D-GARCH-GG model,Curvelet transform,MAP estimation,Speckle,Synthetic Aperture Radar | Noise reduction,Autoregressive model,Speckle pattern,Pattern recognition,Computer science,Synthetic aperture radar,Gaussian,Artificial intelligence,Estimator,Wavelet transform,Curvelet | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 1 |
PageRank | References | Authors |
0.35 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| maryam amirmazlaghani | 1 | 20 | 4.73 |
| hamidreza amindavar | 2 | 215 | 36.34 |