Contourlet-based Despeckling for SAR Image Using Hidden Markov Tree and Gaussian Markov Models
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.
Guozhong Chen Xingzhao Liu
EE Department,Shanghai Jiao Tong University,China
国际会议
首届亚太合成孔径雷达会议(1st Asian and Pacific Conference on Synthetic Aperture Radar Proceedings)
安徽黄山
英文
2007-11-05（万方平台首次上网日期，不代表论文的发表时间）