Maximum contrast analysis for nonnegative blind source separation
Maximum contrast analysis for nonnegative blind source separation
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DOI：
10.1016/j.camwa.2011.09.003
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Zuyuan Yang;Yong Xiang;S. Xie
中科院分区：
文献类型：
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作者：
Zuyuan Yang;Yong Xiang;S. Xie
本文提出了一种非负盲源分离的最大对比度分析（MCA）方法，其中混合矩阵和源信号均为非负。我们首先证明了源信号的对比度大于混合信号的对比度。出于这一观察，我们提出了一个基于MCA的成本函数。它进一步表明，分离矩阵可以通过最大化所提出的成本函数。然后，我们推导出一个迭代行列式最大化算法估计的分离矩阵。在两个源的情况下，一个封闭形式的解决方案存在，并推导出。与现有的盲源分离方法不同，该方法既不需要独立性假设，也不需要源信号的稀疏性要求。通过对X射线图像、遥感图像、红外光谱图像和真实世界荧光显微图像的实验，说明了新方法的有效性。
In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.