SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field
Computers & Mathematics with Applications
Image denoising using mixtures of projected Gaussian scale mixtures
IEEE Transactions on Image Processing
Wavelet-based SAR image despeckling and information extraction, using particle filter
IEEE Transactions on Image Processing
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Wavelet domain image restoration with adaptive edge-preserving regularization
IEEE Transactions on Image Processing
Multiscale MAP filtering of SAR images
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
SAR amplitude probability density function estimation based on a generalized Gaussian model
IEEE Transactions on Image Processing
SAR image filtering based on the heavy-tailed Rayleigh model
IEEE Transactions on Image Processing
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Particle filter (PF) is an effective approach to nonlinear and non-Gaussian Bayesian state estimation and has been successfully applied to wavelet-based synthetic aperture radar (SAR) image despeckling. In this paper, we propose an improved PF despeckling algorithm based on Markov Random Field (MRF) model that can preserve the edge, textural information and structural features of SAR images well. First, we show that the wavelet coefficients of SAR images which exhibit significantly non-Gaussian statistics can be described accurately by generalized Gaussian distribution (GGD) in stationary wavelet domain. Secondly, to amend the weight deviation, MRF model parameters are introduced to redefine the importance weight of the particles. At last, region-divided processing is implemented for the real time application of the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated images and real SAR images.