Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of...

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Veröffentlicht in:IEEE transactions on image processing 2006-11, Vol.15 (11), p.3385-3399
Hauptverfasser: Argenti, F., Bianchi, T., Alparone, L.
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description In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation
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Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>17076398</pmid><doi>10.1109/TIP.2006.881970</doi><tpages>15</tpages></addata></record>
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subjects Adaptive filtering
Algorithms
Applied sciences
Coefficients
Computer Simulation
Detection, estimation, filtering, equalization, prediction
Equations
Exact sciences and technology
Gaussian
generalized Gaussian (GG) modeling
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image resolution
Information Storage and Retrieval - methods
Information, signal and communications theory
Likelihood Functions
Mathematical analysis
Mathematical models
maximum a posteriori (MAP) estimation
Miscellaneous
Models, Statistical
Noise shaping
Normal Distribution
Numerical Analysis, Computer-Assisted
Probability density function
Probability density functions
Radar
Reproducibility of Results
Sensitivity and Specificity
Shape
Signal and communications theory
Signal processing
Signal Processing, Computer-Assisted
Signal representation. Spectral analysis
Signal resolution
Signal, noise
Spatial resolution
Speckle
Statistics
Studies
Synthetic aperture radar
synthetic aperture radar (SAR) images
Telecommunications and information theory
undecimated wavelet decomposition
Wavelet
Wavelet coefficients
title Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling
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