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[Blind source separation (BSS) aims to recover unobserved source signals from their observed mixtures without any information of the mixing system. It is a fundamental problem in signal and image processing. In this chapter, we first introduce the background of BSS, including its history and...
[It is well-known that many real-world signals are nonnegative [1–8], i.e., their sample values are either zero or greater than zero, such as images. Obviously, nonnegativity is different from the statistical information of sources. Depending on the kinds of dependent sources, the nonnegativity...
[Sparsity is an important property shared by many kinds of signals in numerous practical applications. These signals are sparse to some extent in different representation domains, such as time domain, frequency domain or time-frequency domain. In recent years, sparsity has been widely exploited...
[Most existing methods for dependent component analysis only work under the condition that the source signals are nonnegative and/or sparse [1, 2]. Unfortunately, the signals in many real-world applications such as wireless communication systems are neither nonnegative nor sparse. In this...
[In this chapter, we outline the technical issues and challenges in blind separation of mutually correlated sources. This points out directions for future work.]
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