基于独立分量分析的盲非独立图像分离

Wu Guo, Peng Zhang, Runsheng Wang
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引用次数: 0

摘要

一些独立分量分析方法有效地解决了相互独立的混合图像的盲分离问题。但是当源图像在统计上不独立时,这些方法往往失败。提出了一种新的基于复杂度追求的不动点FastICA算法,该算法可以成功地分离不相互独立的混合图像。实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind Non-independent Image Separation Based on Independent Component Analysis
Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.
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