基于NSCT域主分量分析的遥感图像去噪方法

摘要

A new local adaptive threshold estimation method for image denoising based on the Nonsubsampled Contourlet Transform(NSCT) and the Principal Component Analysis (PCA) is proposed in this paper.By utilizing noise energy based on PCA in NSCT domain, a local adaptive threshold is proposed which considering the clustering property of the coefficients for remote sensing image denoising.The simulation experimental results show that the proposed method can effectively reduce Gauss noise in remote sensing image and preserve image edg.Compared with the contourlet hard-thresholding, PCA in contourlet domain and the NSCT hard-thresholding denoising method,the proposed method is obviously superior both in vision and in PSNR.%提出一种新的结合非下采样Contourlet变换(NSCT)和主分量分析(PCA)的图像自适应阈值去噪方法.通过PCA估计NSCT域中的噪声能量,并与NSCT系数的领域信息相结合,构造出自适应阈值对遥感图像进行去噪.仿真实验结果表明,提出的方法与Contourlet硬阈值,基于Contourlet的图像PCA和NSCT硬阈值去噪方法相比能够有效去除遥感图像的高斯噪声,较完整地保持图像的边缘等细节信息,提高了图像的峰值信噪比,图像视觉效果也有明显改善.