中文摘要：

根据图像各子带系数的相关性,提出一种局部自适应的图像小波系数的统计算法,并应用于遥感图像的去噪研究.首先将图像的小波分解系数视为服从广义高斯分布（GGD）的随机变量模型,然后在小波软阈值去噪的基础上,根据图像小波系数在空间上具有聚集性的特点,提出了一种新的局部自适应的算法,结合最大后验概率（MAP）参数估计,用于恢复带噪图像.该算法用于岷江上游植被和土壤类型典型地区—毛儿盖实验区遥感图像的去噪,效果理想,同其他的图像去噪算法相比,它具有较高的峰值信噪比（PSNR）和更好的视觉效果.

英文摘要：

Based on exploiting the correlations among the image wavelet decomposition coefficients in a sub-band, an adaptive statistical model for wavelet image coefficients was presented and applied to the image denoising of Remote Sensing Image. Each wavelet coefficient was firstly modeled as a random variable of a generalized Gaussian distribution （GGD） , then, based on the algorithm of the wavelet soft threshold denoising and according to the characteristics of spa- tial clustering of wavelet decomposition coefficients, a new local adaptive algorithm was proposed and applied to restore the noisy images by estimating the coefficients with maximum a posteriori probability rule （MAP）. The algorithm was applied to denoise the noisy Remote Sensing Image of Maoergai area in the upper Minjiang where contains typical vege- tation and soil. Simulation results showed that the higher peak-signal to noise ratio and the better visual effects were ob- tained as compared to other image denoising methods.