Synthetic Aperture Radar (SAR) Image Denoising With Adaptive Multi-thresholding Method.

Synthetic aperture radar (SAR) is a kind of active coherent high resolution imaging radar system capable of producing high-resolution terrain image from data collected by a physically small aperture antenna. Synthetic aperture radar achieve high angular resolution by integrating the backscattered signal to synthesize the effect of a large aperture antenna, and get the high enough range resolution by pulse compressing techniques.However, because of the coherent imaging mechanism, which determining the noise of image is mainly multiply noise. As to the multiply noise, we must convert the multiply noise to additional noise through logarithm transform.Image denoising via wavelet transform is one success of wavelet applications, where the most important case is how to obtain the optimal threshold. This paper proposes an adaptive, data-driven multi-thresholding for image denoising framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution(GGD) which has been widely used in image processing applications.In this paper, we convert the multiply noise to additional noise through logarithm transform for SAR image. In addition, the logarithm transform changes its statistical characteristic, so we must adjust the mean in the process. In this paper, we deal with the statically characters changing particularly after algorithm transform.In the last, we present the experiment results of additional noise (Lena image) and multiply noise (SAR image).. The experiment results illuminates that the method of this paper may hold on the images edge information and texture characters, in the same time, this method preserves the SAR image structure and may suppress the speckle noise.