SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning
复制标题
基于连续循环旋转的剪切波域稀疏表示SAR图像去噪
DOI：
10.1109/tgrs.2017.2657602
复制
发表时间：
2017-05-01
影响因子：
8.2
通讯作者：
Wang, Xuehu
中科院分区：
文献类型：
--
作者：
Liu, Shuaiqi;Liu, Ming;Wang, Xuehu
如何有效地抑制相干斑噪声已成为遥感图像处理中的关键问题之一。这一问题也严重制约了关键技术的发展，特别是在军事应用等方面。针对基于稀疏表示的图像去噪最优解不具有原始信号空间一一映射的缺点，提出了一种基于连续循环旋转的Searlet域稀疏表示的合成孔径雷达(SAR)图像去噪方法。首先，对含有噪声的SAR图像进行Searlet变换。其次，利用基于循环旋转理论的稀疏表示模型，构造了一种新的最优去噪模型。最后，采用交替迭代算法求解最优去噪模型，得到去噪后的图像。实验结果表明，该方法不仅有效地抑制了相干斑噪声，提高了去噪SAR图像的峰值信噪比，而且明显改善了SAR图像的视觉效果，特别是增强了SAR图像的纹理。
How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.