计算机科学
阈值
盲信号分离
源分离
稀疏逼近
信号(编程语言)
独立成分分析
信号处理
分解
模式识别(心理学)
人工智能
匹配追踪
算法
图像(数学)
压缩传感
数字信号处理
计算机网络
生态学
频道(广播)
计算机硬件
生物
程序设计语言
作者
M.J. Fadili,Jean-Luc Starck,J. Bobin,Y. Moudden
出处
期刊:Proceedings of the IEEE
[Institute of Electrical and Electronics Engineers]
日期:2010-06-01
卷期号:98 (6): 983-994
被引量:160
标识
DOI:10.1109/jproc.2009.2024776
摘要
This paper gives essential insights into the use of sparsity and morphological diversity in image decomposition and source separation by reviewing our recent work in this field. The idea to morphologically decompose a signal into its building blocks is an important problem in signal processing and has far-reaching applications in science and technology. Starck , proposed a novel decomposition method-morphological component analysis (MCA)-based on sparse representation of signals. MCA assumes that each (monochannel) signal is the linear mixture of several layers, the so-called morphological components, that are morphologically distinct, e.g., sines and bumps. The success of this method relies on two tenets: sparsity and morphological diversity. That is, each morphological component is sparsely represented in a specific transform domain, and the latter is highly inefficient in representing the other content in the mixture. Once such transforms are identified, MCA is an iterative thresholding algorithm that is capable of decoupling the signal content. Sparsity and morphological diversity have also been used as a novel and effective source of diversity for blind source separation (BSS), hence extending the MCA to multichannel data. Building on these ingredients, we will provide an overview the generalized MCA introduced by the authors in and as a fast and efficient BSS method. We will illustrate the application of these algorithms on several real examples. We conclude our tour by briefly describing our software toolboxes made available for download on the Internet for sparse signal and image decomposition and separation.
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