Contourlet-based despeckling for SAR image using hidden Markov tree and Gaussian Markov models
Guozhong Chen, Xingzhao Liu · 2007
The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we propose a contourlet-based despeckling method for the SAR image using the Hidden Markov Tree (HMT) and Gaussian Markov models. The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. The HMT and Gaussian Markov models will reflect the correlations of the contourlet coefficients not only across scales and directions but also between neighbors. The experimental results show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect.