基于复Shearlet域高斯混合模型的SAR图像去噪

 结合双树复小波的平移不变性、多分辨率性和剪切波变换的灵活可选的多方向性,提出一种新的图像表达方法——复Shearlet变换。针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像的相干噪声特点,建立了复Shearlet系数域的高斯混合模型(Gaussian Mixture Model,GSM),在此基础上应用贝叶斯最小二乘法进行系数估计,最后进行复Shearlet反变换得到去噪以后的SAR图像。仿真结果和分析表明:本文提出的算法相比其他变换域去噪算法,不仅去噪后的图像的峰值信噪比(Peak Signal to Noise Ratio, PSNR)有所提高,而且去噪后的图像更平滑,且与Shearlet域高斯混合模型相比,本文算法速度快了两倍多。

SAR Image De-noising Based on Complex Shearlet Transform Domain Gaussian Mixture Model

Using the characteristics of translation invariance, multi-resolution of dual tree complex wavelet transform and the more flexible, multi-selectivity of Shearlet transformation, this paper proposes a new algorithm called complex Shearlet transform. A Gaussian mixture model is introduced in order to capture the local coefficients of the complex Shearlets of synthetic aperture radar (SAR) images. The coefficients are estimated by Bayesian least squares estimator based on the model. Then, an inverse complex Shearlet transform is applied to the modified coefficients to get the SAR image after de-noising. The simulation effect and the analysis of the test results show that, compared with other de-noising methods, this algorithm has a better peak signal to noise ratio (PSNR) and the de-noised images are smoother. The computing speed is more than twice as fast as the method using the Shearlet domain Gaussian mixture model de-noising method.