刘杰, 李志农, 范涛, 杨诚. 变分贝叶斯独立分量分析在含噪图像盲分离中的应用研究[J]. 南昌航空大学学报(自然科学版), 2018, 32(1): 9-13. DOI: 10.3969/j.issn.1001-4926.2018.01.002
引用本文: 刘杰, 李志农, 范涛, 杨诚. 变分贝叶斯独立分量分析在含噪图像盲分离中的应用研究[J]. 南昌航空大学学报(自然科学版), 2018, 32(1): 9-13. DOI: 10.3969/j.issn.1001-4926.2018.01.002
LIU Jie, LI Zhi-nong, FAN Tao, YANG Cheng. Variational Bayesian Independent Component Analysis and Its Application in Blind Separation Method of Noisy Images[J]. Journal of nanchang hangkong university(Natural science edition), 2018, 32(1): 9-13. DOI: 10.3969/j.issn.1001-4926.2018.01.002
Citation: LIU Jie, LI Zhi-nong, FAN Tao, YANG Cheng. Variational Bayesian Independent Component Analysis and Its Application in Blind Separation Method of Noisy Images[J]. Journal of nanchang hangkong university(Natural science edition), 2018, 32(1): 9-13. DOI: 10.3969/j.issn.1001-4926.2018.01.002

变分贝叶斯独立分量分析在含噪图像盲分离中的应用研究

Variational Bayesian Independent Component Analysis and Its Application in Blind Separation Method of Noisy Images

  • 摘要: 传统的独立分量分析不具有抗干扰性,而且需在源个数已知的条件下,才能进行含噪图像盲分离。针对此不足,本研究提出了一种基于变分贝叶斯独立分量分析的含噪图像盲分离方法。所提算法与传统的分离方法相比,可直接有效分离含噪图像,且具有较强的抗干扰性。此外,该算法根据不同模型的信度估计信源数。实验结果表明,提出的方法是非常有效的。

     

    Abstract: The traditional independent component analysis does not have the anti-interference ability, and it needs to make the noisy image blind separation under the condition that the number of sources is known. To solve this problem, a blind separation method is proposed for noisy images based on variational Bayesian independent component analysis (VbICA). Contrast with the conventional separation methods, the proposed algorithm can directly and effectively separate the noisy images and has strong anti-interference ability. In addition, the algorithm estimates the number of sources based on the reliability of different models. The experiment results show that this algorithm is very effective.

     

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