In this paper a wavelet-based speckle removing algorithm is resented and tested on Synthetic Aperture Radar (SAR) images. The distribution of a noise free image in wavelet domain is modeled as a General- Gauss-Markov random field. An unsupervised stochastic model-based approach to image denoising is presented. The parameters of Gaussian distribution and General-Gauss-Markov Random fields are estimated from incomplete data using mixtures of wavelet coefficients, if the observed area is homogeneous. The presented wavelet-based method efficiently removes noise from SAR images.
COBISS.SI-ID: 11905558
This letter presents the despeckling of SAR images within the bandelet and contourlet domains. A model-based approach is presented for despeckling of SAR images. The speckle-reduced estimate is found using the first order Bayesian inference and the best model's parameters are estimated using the second order Bayesian inference. The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and the objective measurements.
COBISS.SI-ID: 0030620081
This paper presents the despeckling of Single Look Complex (SLC) Synthetic Aperture Radar (SAR) images using non-quadratic regularization. The objective function consists of the image model, a gradient, and a prior model. The Huber-Markov random field models the prior. The numerical solution is achieved through extensions of half-quadratic regularization methods using complex-valued SAR data. The proposed method using the Huber-Markov random field prior together with non-quadratic regularization shows the superior results on SLC synthetic and actual SAR images.
COBISS.SI-ID: 0029820082
In this letter, a comparison between three different despeckling methods based on Bayesian approach and Gibbs random fields is made. The used methods are Gauss-Markov random field and Auto-binomial modeling which operate in the image domain and the Gauss-Markov random field approach which operates in the wavelet domain.
COBISS.SI-ID: 0030820082
This paper presents a model based despeckling of Synthetic Aperture Radar (SAR) images using Bayesian analysis. The SAR image is despeckled using first order Bayesian inference. The novelty in this paper is an auto-binominal model. The best auto-binomial model for approximating the texture parameters in SAR images is found by using second order Bayesian inference. The experimental results show that the proposed method preserves the textural features and removes noise significantly in the homogeneous and heterogeneous regions.
COBISS.SI-ID: 20076563