Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion
Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion
复制标题
用于医学图像去噪和融合的字典学习的组稀疏表示
DOI：
10.1109/tbme.2012.2217493
复制
发表时间：
2012-12-01
影响因子：
4.6
通讯作者：
Fang, Leyuan
中科院分区：
文献类型：
--
作者：
Li, Shutao;Yin, Haitao;Fang, Leyuan
近年来，稀疏表示在各个领域引起了广泛的兴趣。然而，标准稀疏表示不考虑内在结构，即，非零元素出现在集群中，称为群稀疏性。此外，还没有字典学习方法的群体稀疏表示考虑到空间的几何结构的原子。在本文中，我们提出了一种新的字典学习方法，称为字典学习与组稀疏和图正则化（DL-GSGR）。首先，原子的几何结构被建模为图正则化。然后，结合群稀疏性和图正则化，提出了DL-GSGR，通过交替进行群稀疏编码和字典更新来解决。通过这种方式，学习字典的组相干性可以被强制足够小，使得任何信号都可以被有效地组稀疏编码。最后将基于DL-GSGR的群稀疏表示应用于三维医学图像的去噪和图像融合。具体地，在3-D医学图像去噪中，利用3-D处理机制（使用邻近切片之间的相似性）和时间正则化（以颠倒邻近切片之间的相关性）。三维图像去噪和图像融合的实验结果表明，我们提出的去噪和融合方法的优越性。
Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.