提出了一个新的有效的基于Contourlet变换的遥感图像去噪方法。对有噪图像进行Contourlet分解;对Contourlet变换系数引入一个几何先验模型,结合噪声和有用信号的条件分布进行贝叶斯估计,得到每一系数作为有用信号的后验概率,以之作为修正因子修正小波萎缩因子;对重构图像进行递归循环运算处理。仿真实验结果表明, 去噪后图像去除了常见的伪吉布斯现象,峰值信噪比提高了1～2 dB。

We propose a new and efficient denoising method for remote sensing image based on the contourlet transform. Firstly, we introduce a geometrical prior model, and estimate the Bayesian framework by combining the noise with the useful signal. Then, we compute each coefficient as the probability of evailable signal, modifying the wavelet shrinkage factor. Finally, we reconstruct multi-resolution wavelet coefficient and process it using recursive cycle spinning. The experimental results show that the denoised image eliminates pseudo-Gibbs phenomena, the peak signal-to-noise ratio increases 1～2 dB.