The framework of this article is to introduce a new efficient Blind Source Separation (BSS) method that handles mixtures of noise-contaminated independent / dependent sources. In order to achieve that, one can minimize a criterion that fuses a separating part, based on Kullback–Leibler divergence to set apart the observed mixtures of either dependent or independent sources, with a regularization part that employs the bilateral total variation (BTV) for the purpose of denoising the observations. The proposed algorithm utilizes a primal-dual algorithm to remove the noise, while a gradient descent method is implemented to retrieve the source signals. Our algorithm has shown its effectiveness and efficiency toward the noisy dependent / independent sources and also surpassed the standard BSS algorithms through different experimental results.

本文的框架是介绍一种新的高效盲源分离 (BSS) 方法，该方法可以处理受噪声污染的独立/依赖源的混合。为了实现这一点，人们可以最小化融合分离部分的标准，基于 Kullback-Leibler 散度来区分观察到的依赖或独立源的混合物，使用双边总变差 (BTV) 的正则化部分去噪观察的目的。所提出的算法利用原始对偶算法去除噪声，同时实现梯度下降法来检索源信号。我们的算法已经展示了它对噪声依赖/独立源的有效性和效率，并且通过不同的实验结果也超过了标准的 BSS 算法。