Researches On The SAR Image Denoising Methods Based On Multiscale Analysis

The impact of coherent speckle noise (multiplicative noise) in SAR image makes low Signal-to-Noise of image (usually less than 1), if the speckle noise suppression of improper handling will have a direct impact on the follow-up.The wavelet analysis is a new method followed by Fourier Transform in harmonic analysis, and it has been widely and successfully applied in signal and image processing field in recent years. It is widely acknowledged that wavelet is the optimal base for the functions with point shape singularity, and its coefficients are sparse. But in high dimensional cases, wavelet analysis cannot take advantage of the geometrical features that data contained inherently. It is not the optimal or the sparsest representation of the function and always damages information of anisotropic image details.To solve this problem, the Multiscale Geometric Analysis (MGA) emerges as the times require which takes up with the optimal representation of high dimensional functions. In this paper we investigated the Wedgelet, a new tool of multiscale geometric analysis. A novel method of SAR image denoising based on Wedgelet and Complex Wavelet Transform (Wedgelet-CWT) is proposed. On the one hand, it takes advantage of the good approximation features of Wedgelet to get edge information, and on the other hand it uses CWT to capture texture information in an original image. we load a different standard deviation of the Gaussian white noise on experimental images, and use PSNR and NSNR two indexs to judge the algorithms. The results show that the algorithm of Wedgelet-CWT denoise, has a certain degree of decrease on additive noise.