基于SURE-LET和非张量积小波的遥感图像去噪

针对遥感图像中的高斯噪声，提出了基于SURE-LET和非张量积小波的去噪方法，主要包括图像在非张量积小波下的分解、各个子带在不同阈值函数下的处理以及它们最优的线性组合3个步骤．通过选择合适的非张量积小波滤波器参数，使无噪遥感图像和噪声在变换分解中得到的小波系数分离较好，去除噪声对应的小波系数时被去除的无噪图像对应的小波系数较少，从而取得更好的去噪效果．实验结果表明：此方法用于高斯噪声的遥感图像的去噪不仅速度很快，而且去噪效果优于传统基于张量积小波的SURE-LET方法．

SURE-LET and non-tensor wavelets based remote sensing image denoising

A novel method to address the Gaussian noise of remote sensing image using non-tensor wavelet and SURE-LET was presented,which mainly contained three parts: the non-tensor wavelet decomposition,coefficients shrinkage of each subbands using the threshold functions,and estimating the optimal combination weights of the processed subbands.The non-tensor wavelet filters could be represented in the parametric form,by using appropriate,the non-tensor wavelet coefficients of noise free remote sensing images and noise were separated better than the traditional tensor wavelet coefficients.As a result,when using coefficient shrinkage technique to remove the noise,more noise free image coefficients could be reserved.Consequently,better denoising performance could be obtained.Experimental results show that by combining non-tensor wavelet and SURE-LET,the denoising procedure is very fast and denoising performance in sense of PSNR is prior to tensor wavelet and SURE-LET.