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.
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.
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2013年第4期587-591,共5页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家自然科学基金项目(41071265)
高等学校博士学科点专项科研基金项目(20105122110006)
