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Embodying information into images by an MMI-based independent component analysis algorithm

机译:基于MMI的独立分量分析算法将信息体现为图像

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The signals measured by multi-sensors are always the mixtures of several independent sources. Therefore, it is necessary to separate them from each other for practical applications. Independent component analysis is a novel signal processing method presented in dealing with such problems. Recently, it is also found very useful in many other problems such as biomedical signal separation, communication and multimedia information processing. In this paper, we gave a minimizing mutual information based ICA algorithm and then applied it into information hiding or digital watermarks. We consider the original image as a mixture of some independent character images. So we can embodying the watermark into the separated images and then remix them as a watermarked image. Simulations results show the validity in embodying information into images.
机译:多传感器测量的信号始终是几个独立源的混合物。因此,有必要为实际应用彼此分开。独立分量分析是在处理此类问题时提出的新型信号处理方法。最近,在许多其他问题中,也发现它非常有用,例如生物医学信号分离,通信和多媒体信息处理。在本文中,我们能够最大限度地减少基于互信息的ICA算法,然后将其应用于信息隐藏或数字水印。我们将原始图像视为一些独立字符图像的混合。所以我们可以将水印体现到分离图像中,然后将它们混合为水印图像。仿真结果显示了将信息体现为图像的有效性。

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