基于提升格式小波包变换的SAR图像去噪

基于信号和噪声在提升格式小波分解中呈现出的不同特性,提出了一种新的小波包去噪算法。该算法采用提升格式小波对SAR图像进行最优小波包分解,并计算每个子频带的能量范数,然后根据软阈值法和能量范数区分信号和噪声,达到去除噪声的目的。试验结果表明,该算法对SAR图像具有较好的去噪效果,不仅可以去除图像中的大部分噪声,而且可以较好地保留图像纹理的细节信息。与传统小波包算法相比,其计算速度快了一倍左右。

Denoising of SAR Images Based on Lifting Scheme Wavelet Packet Transform

According to the different characteristics that signal and noise exhibit during the wavelet decomposition, a new denoising method based on the lifting scheme wavelet packet decomposition is presented. In this method, the SAR images are decomposed by using the best wavelet packet and the norm of each sub-band are calculated, signals and noise can be discriminated based on the norm and soft-threshold method, and the images can be denoised. Experimental shows that the proposed algorithm is of excellent performance in denoising SAR images, and can remove most noise of images with well-kept texture details information. The calculating speed of the method is twice the speed of the general wavelet packet transform algorithm.