Sparse directional image representations using the discrete shearlet transform
Sparse directional image representations using the discrete shearlet transform
复制标题
DOI：
10.1016/j.acha.2007.09.003
复制
发表时间：
2008-07-01
影响因子：
2.5
通讯作者：
Lim, Wang-Q
中科院分区：
文献类型：
--
作者：
Easley, Glenn;Labate, Demetrio;Lim, Wang-Q
尽管传统小波在信号处理应用中取得了显著的成功，但人们普遍认为传统小波在处理包含分布不连续点（如边缘）的多维信号时并不十分有效。为了克服这一限制，必须使用具有更高方向灵敏度和各种形状的基元，以便能够捕捉多维现象的内在几何特征。本文介绍了一种新的离散多尺度定向表示，称为离散shearlet变换。该方法基于shearlet变换，将多尺度方法的强大功能与捕获多维数据的几何形状的独特能力相结合，在表示包含边缘的图像方面具有最佳效率。我们描述了实现shearlet变换的两种不同方法。数值实验表明，离散剪切波变换在性能和计算效率方面在去噪应用中具有很强的竞争力。Elsevier Inc.出版。
In spite of their remarkable success in signal processing applications, it is now widely acknowledged that traditional wavelets are not very effective in dealing multidimensional signals containing distributed discontinuities such as edges. To overcome this limitation, one has to use basis elements with much higher directional sensitivity and of various shapes, to be able to capture the intrinsic geometrical features of multidimensional phenomena. This paper introduces a new discrete multiscale directional representation called the discrete shearlet transform. This approach, which is based on the shearlet transform, combines the power of multiscale methods with a unique ability to capture the geometry of multidimensional data and is optimally efficient in representing images containing edges. We describe two different methods of implementing the shearlet transform. The numerical experiments presented in this paper demonstrate that the discrete shearlet transform is very competitive in denoising applications both in terms of performance and computational efficiency. Published by Elsevier Inc.