基于SURE-LET和非张量积小波的遥感图像去噪

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

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.

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 estimatingthe 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 coeffi-cients. 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 pro- cedure is very fast and denoising performance in sense of PSNR is prior to tensor wavelet and SURE- LET.