基于UWT和独立分量分析的含噪盲源分离OA北大核心CSCDCSTPCD
Noisy blind source separation based on undecimated wavelet transform and independent component analysis
提出了基于UWT（非抽样小波变换）去噪与FastICA（快速独立分量分析）算法相结合的含噪盲源分离方法，采用先去噪后分离的方式实现了在加性高斯噪声环境下混合图像的盲分离。仿真结果表明，该方法能很好地从加性高斯噪声中分离出源图像，与曲波阈值去噪后的FastICA方法相比较，该方法能获得更好的峰值信噪比。
This paper proposes a method to realize noisy blind source separation based on UWT(Undecimated Wavelet Transform)denoising and FastICA(Independent Component Analysis). The method employs a model of ICA after denoising to implement noisy image separation under the environment of additive Gaussian noise. The simulation results show that the proposed method can separate noisy mixed images efficiently. Compared with the method based on curvelet denosing before I…查看全部>>
蔡伟华;何选森
湖南大学 信息科学与工程学院，长沙 410082湖南大学 信息科学与工程学院，长沙 410082
信息技术与安全科学
盲源分离非抽样小波变换(UWT)快速独立分量分析曲波变换峰值信噪比
blind source separationUndecimated Wavelet Transform(UWT)Fast Independent Component Analysis (FastICA)curvelet transformPeak Signal-to-Noise Ratio(PSNR)
《计算机工程与应用》 2016 (16)
180-185,6
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