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arXiv 1011.4615cs.CV
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广义树基小波变换

Generalized Tree-Based Wavelet Transform

Technion – Israel Institute of Technology(以色列理工学院)

Idan Ram, Michael Elad, Israel Cohen

摘要:

本文提出了一种新的小波变换,它适用于定义在图、高维数据和网络上的函数。所提出的方法推广了[1]中提出的Haar类变换,并通过层次树定义,假设该层次树捕获输入数据的几何和结构。它适用于使用常见的一维(1D)小波滤波和抽取方案,它可以采用不同的小波滤波器的修改版本的数据。在该小波分解方案的每一级中,在对近似系数进行滤波之前,将从树导出的置换应用于近似系数。我们提出了一种树的构造方法,在变换域中的输入函数的有效表示。我们表明,该变换是更有效的比1D和二维(2D)可分离小波变换在表示图像。我们还探讨了所提出的变换的图像去噪的应用,并表明,结合子图像平均计划,它实现了去噪的结果是类似的K-SVD算法获得的。

英文摘要:

In this paper we propose a new wavelet transform applicable to functions defined on graphs, high dimensional data and networks. The proposed method generalizes the Haar-like transform proposed in [1], and it is defined via a hierarchical tree, which is assumed to capture the geometry and structure of the input data. It is applied to the data using a modified version of the common one-dimensional (1D) wavelet filtering and decimation scheme, which can employ different wavelet filters. In each level of this wavelet decomposition scheme, a permutation derived from the tree is applied to the approximation coefficients, before they are filtered. We propose a tree construction method that results in an efficient representation of the input function in the transform domain. We show that the proposed transform is more efficient than both the 1D and two-dimensional (2D) separable wavelet transforms in representing images. We also explore the application of the proposed transform to image denoising, and show that combined with a subimage averaging scheme, it achieves denoising results which are similar to those obtained with the K-SVD algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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