Wavelet-based SAR image despeckling and information extraction, using particle filter
IEEE Transactions on Image Processing
Region-Based Active Contours with Exponential Family Observations
Journal of Mathematical Imaging and Vision
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
Journal of Mathematical Imaging and Vision
Smooth adaptation by sigmoid shrinkage
Journal on Image and Video Processing
SAR speckle mitigation by fusing statistical information from spatial and wavelet domains
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
X2random fields in space and time
IEEE Transactions on Signal Processing
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
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Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal