1st International ICST Conference on Communications and Networking in China

Research Article

The Blind Separation of Noisy Mixing Image Based on FASTICA and Wavelet Transform

  • @INPROCEEDINGS{10.1109/CHINACOM.2006.344830,
        author={Li  Hongyan and Ma  Jianfen and Wu  Juanping and Wang  Huakui},
        title={The Blind Separation of Noisy Mixing Image Based on FASTICA and Wavelet Transform},
        proceedings={1st International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2007},
        month={4},
        keywords={Independent component analysis; Wavelet threshold; Blind source separation},
        doi={10.1109/CHINACOM.2006.344830}
    }
    
  • Li Hongyan
    Ma Jianfen
    Wu Juanping
    Wang Huakui
    Year: 2007
    The Blind Separation of Noisy Mixing Image Based on FASTICA and Wavelet Transform
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2006.344830
Li Hongyan1,*, Ma Jianfen1, Wu Juanping1, Wang Huakui1
  • 1: College of Information Engineering of taiyuan university of technology, Taiyuan
*Contact email: lhy6018@163.com

Abstract

Blind source separation problem have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the additive noise is negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this paper, a method is proposed of combining wavelet threshold de-noising and independent component analysis to the blind source separation problem for mixing images corrupter with white noise. We first use wavelet threshold to de-noise and then use a new blind separation algorithm of FASTICA to separate the wavelet de-noised images. The result shows that this method may reduce the affect of noise and improve the signal-noise ratio (SNR) of separation images, accordingly renew the original images.