基于局部混合滤波的SAR图像去噪

相干斑噪声是合成孔径雷达(synthetic aperture radar， SAR)成像系统所固有的缺点，严重影响SAR图像的可用性，给后续的图像分割、特征提取和目标识别等工作带来严峻的挑战。结合非下采样方向滤波器和双树复小波变换各自的特点，提出一种新的基于非下采样方向滤波-双树复小波变换的局部混合滤波SAR图像去噪算法,具有多方向和多尺度性，保持了图像的平移不变性，改善了图像的视觉效果。与其他算法不同,本文算法采用非下采样方向滤波器级联双树复小波的方法，不仅对每次产生的高频分量进行去噪，还对变换所产生的低频分量进行滤波去噪。实验结果表明：与使用同级双树复小波-轮廓波变换加软阈值去噪相比，本文算法的峰值信噪比提高2 dB;与使用轮廓波加循环平移（cycle spinning， CS）软阈值算法去噪相比，本文算法去噪后的图像不仅峰值信噪比有所提高，而且去噪后的图像更为平滑，抑制了人造纹理产生，视觉效果得到了明显改善。

SAR image de-noise base on local hybrid filter

The existence of speckle noise has seriously affected the availability of synthetic aperture radar (SAR) images and poses serious challenges to the subsequent image segmentation, feature extraction and target recognition. Combining the features of nonsubsampled directional filter and dual-tree complex wavelet transform, a SAR image de-noise algorithm based on the advantages of the two algorithms mentioned above is proposed, which is a local hybrid filter under nonsubsampled directional filter bank and dual-tree complex wavelet transform de-noise algorithm. The new algorithm holds the features of multi-direction and multi-scale nature of signal, can keep the translation invariance of the image, and improves the image visual effects. The algorithm uses the dual-tree complex wavelet filter following the nonsubsampled direction filter. The difference is that the new algorithm can not only de-noise for the high frequency, but also for the low-frequency part of the multi-resolution multi-frequency band filter for the noise existed in both the high-frequency and the low-frequency. The experiment results show that, compared with the dual-tree complex wavelet-Contourlet transform and soft threshold method, the peak signal-to-noise ratio (PSNR) is increased by 2 dB, and compared with the Contourlet with cycle spinning and soft threshold method, the PSNR is better and the de-noise image is more smooth; moreover, man-made textures are restrained and a significant improvement in the visual effects is obtained.