Separation of Nonlinear Image Mixtures by Denoising Source Separation

  • Mariana S. C. Almeida
  • Harri Valpola
  • Jaakko Särelä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3889)


The denoising source separation framework is extended to nonlinear separation of image mixtures. MLP networks are used to model the nonlinear unmixing mapping. Learning is guided by a denoising function which uses prior knowledge about the sparsity of the edges in images. The main benefit of the method is that it is simple and computationally efficient. Separation results on a real-world image mixture proved to be comparable to those achieved with MISEP.


Source Image Multilayer Perceptrons Source Separation Linear Separation Source Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mariana S. C. Almeida
    • 1
  • Harri Valpola
    • 2
  • Jaakko Särelä
    • 3
  1. 1.Instituto de TelecomunicaçõesLisboaPortugal
  2. 2.Laboratory of Computational EngineeringHelsinki University of Technology, HUTEspooFinland
  3. 3.Adaptive Informatics Research Centre, Laboratory of Computer and Information ScienceHelsinki University of Technology, HUTEspooFinland

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