Gauss-Markov Random Field model for non-quadratic regularization of complex SAR images

This paper presents despeckling and information extraction using non-quadratic regularization. The novelty of this paper is that instead of the Gaussian prior model a Gauss-Markov random field model is chosen, because it can efficiently model textures in the images. The iterative procedure consist of noise-free image and texture parameter. The experimental results show that the proposed method satisfactorily removes noise form synthetic and real SAR images and is comparable with the state of the art methods using objective measurements on synthetic SAR images. Key–Words: despeckling, non-quadratic regularization. synthetic aperture radar, Gauss Markov Random Fields