Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews]
Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews]
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DOI：
10.1109/msp.2011.941842
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发表时间：
2011-08
影响因子：
14.9
通讯作者：
M. Wakin
中科院分区：
文献类型：
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作者：
M. Wakin
尽管这本书不到300页，但它涵盖了令人印象深刻的各种主题。书的前半部分致力于稀疏表示的调查。在这里，作者移动得相当快，从经典的连续和离散小波变换的描述开始，然后进行到更高级的主题，如小波提升，小波包，复小波和非抽取小波。他们最终讨论了现代脊波和曲波变换的图像和高维信号处理。沿着的方式，简要说明每个变换的优点和缺点，例如，一些是更好的压缩，而其他的是优选的特征提取和噪声去除，并选择的细节给出了如何每个可以计算实现。每一章都以一组数值实验结束，这些实验向读者展示了一些基本概念，并且所有复制这些实验所需的软件都可以在网站上找到。
Although it has fewer than 300 pages,this book covers an impressive variety of topics. The first half of the book is devoted to a survey of sparse representations. Here, the authors move rather quickly, beginning with descriptions of the classical continuous and discrete wavelet transforms, and then proceed to more advanced topics such as wavelet lifting, wavelet packets, complex wavelets,and undecimated wavelets. They culminate with a discussion of the modern ridgelet and curvelet transforms for image and higher-dimensional signal processing. Along the way, brief explanations are provided for the advantages and disadvantages of each transform¿for example, some are better for compression, while others are preferable for feature extraction and noise removal¿and selected details are given for how each may be implemented computationally. Each chapter concludes with a set of numerical experiments that demonstrate some of the basic concepts to the reader, and all software necessary toreproduce these experiments is available on a website.