Unsupervised neural network for nonlinear noisy image separation

  • Authors:

  • Xiaowei Zhang;Jianming Lu;Takashi Yahagi

  • Affiliations:

  • Chiba University, Chiba, Japan;Chiba University, Chiba, Japan;Chiba University, Chiba, Japan

  • Venue:
  • SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
  • Year:
  • 2007

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Abstract

Nonlinear Blind Source Separation (NLBSS) has received much research attention recently due to the emergence of simple, powerful algorithms that show promise in practical applications. In this paper, we consider the problem of nonlinear noisy mixed images. We will propose EM(Expectation-Maximization) as a learning algorithm of Self-Organizing Maps (SOM) for NLBSS problem. It has the benefits of both EM and SOM algorithms, without constraints on source signals. We first used the proposed approach to denoise the mixed images and then use it again to achieve separation. We show in the simulation that the SOM-based approach can provide a solution to the nonlinear noisy image separation problem.