基于稀疏表示的Shearlet域SAR图像去噪

该文通过分析SAR图像的噪声成因以及其斑点噪声模型,结合图像的稀疏表示理论提出一种基于稀疏表示的Shearlet域SAR图像去噪算法。算法从整体上对SAR图像进行去噪:首先对SAR图像进行Shearlet变换,然后利用稀疏表示模型构造出去噪的最优化模型,在此基础上进行迭代去噪,然后重构SAR图像得到去噪后的图像。实验结果表明:该文所提出的算法不仅可以显著去除相干斑噪声,提高去噪图像的峰值信噪比(Peak Signal toNoise Ratio,PSNR),还明显地改善了图像的视觉效果,更好地保留了图像纹理信息。

Shearlet Domain SAR Image De-noising via Sparse Representation

After analyzing the causes of SAR image noise and speckle model,a SAR image de-noising method is presented in Shearlet domain from the theory of image sparse representation.The proposed algorithm is to de-noise SAR image from the entire image information: firstly,Shearlet transform is applied to the noise SAR image,then,the de-noised Shearlet coefficients are got based on iterative de-noising algorithm from noise optimization model which constructed by the model of sparse representation of the SAR image,finally,the clean SAR image is obtained from the de-nosing Shearlet coefficients.The experimental results show that the proposed algorithm can suppress speckle and improve the PSNR of de-noised image significantly,as well as improve visual effect of the image and retain the image texture information better.