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A Blind Separation Method of Noised Image Based on Neural Network Nonlinear Filtering and Independent Component Analysis

机译:基于神经网络非线性滤波和独立分量分析的噪声图像盲分离方法

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摘要

The main objective of this work is to develop a new method for the blind separation of the noised image. A nonlinear neural network and independent component analysis (ICA) algorithm are combined. The neural network filter is used to remove the noise and ICA algorithm is used for the blind separation of the mixed image. But the effect of pre-filter is different from the post-filter. By comparing the experimental results, pre-filter is proved to be more effective. The research work is helpful for the blind source separation of the multidimensional signal.
机译:这项工作的主要目的是开发一种新方法来对噪声图像进行盲分离。结合了非线性神经网络和独立成分分析(ICA)算法。神经网络滤波器用于去除噪声,ICA算法用于混合图像的盲分离。但是前置滤波器的效果与后置滤波器不同。通过比较实验结果,预过滤器被证明是更有效的。研究工作对多维信号的盲源分离有帮助。

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