Image denoising via adaptive eigenvectors of graph Laplacian
Image denoising via adaptive eigenvectors of graph Laplacian
复制标题
通过图拉普拉斯自适应特征向量进行图像去噪
DOI：
10.1117/1.jei.25.4.043019
复制
发表时间：
2016-07
影响因子：
1.1
通讯作者：
Zhao Li
中科院分区：
文献类型：
--
作者：
Chen Ying;Tang Yibin;Xu Ning;Zhou Lin;Zhao Li
抽象的。提出了一种基于自适应图拉普拉斯特征向量的图像去噪方法。与传统EGL方法中所用特征向量的平凡参数设置不同，该方法在整个去噪过程中自适应地选择特征向量。具体地说，首先利用噪声图像的特征向量构建粗糙图像，其中特征向量通过使用干净图像的偏差估计来选择。随后，引导图像被有效地恢复与噪声和粗糙图像的加权平均。在该操作中，自适应地获得平均系数以将引导图像的偏差设置为接近干净图像的偏差。最后，去噪图像是由一个组稀疏模型与引导图像的模式，其中的特征向量选择的噪声偏差的误差控制。此外，一个改进的组正交匹配追踪算法的发展，有效地解决上述组稀疏模型。实验结果表明，该方法不仅提高了EGL方法的实用性，降低了参数设置的依赖性，而且优于一些成熟的去噪方法，特别是对于偏差较大的噪声。
Abstract. An image denoising method via adaptive eigenvectors of graph Laplacian (EGL) is proposed. Unlike the trivial parameter setting of the used eigenvectors in the traditional EGL method, in our method, the eigenvectors are adaptively selected in the whole denoising procedure. In detail, a rough image is first built with the eigenvectors from the noisy image, where the eigenvectors are selected by using the deviation estimation of the clean image. Subsequently, a guided image is effectively restored with a weighted average of the noisy and rough images. In this operation, the average coefficient is adaptively obtained to set the deviation of the guided image to approximately that of the clean image. Finally, the denoised image is achieved by a group-sparse model with the pattern from the guided image, where the eigenvectors are chosen in the error control of the noise deviation. Moreover, a modified group orthogonal matching pursuit algorithm is developed to efficiently solve the above group sparse model. The experiments show that our method not only improves the practicality of the EGL methods with the dependence reduction of the parameter setting, but also can outperform some well-developed denoising methods, especially for noise with large deviations.