Error-Diffusion Binarization for Neural Networks

  • André Granger
  • Tigran Galstyan
  • Roger A. Lessard


In optical implementation of neural networks, binarization is often a necessity. Error-diffusion (ED) is presented has a more reliable binarization technic over hardclipping. We use a simple self-organizing learning algorithm for its demonstration. It is showed in particular that ED keeps more of the original image characteristics upon variable lighting, thus enabling a better extraction of the common features in a set of processed images, and a faster learning convergence of the neural net.


Diffraction Efficiency Variable Lighting Gray Level Image Binarize Version Optical Implementation 
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 Science+Business Media New York 1997

Authors and Affiliations

  • André Granger
    • 1
  • Tigran Galstyan
    • 1
  • Roger A. Lessard
    • 1
  1. 1.COPL, dépt. de PhysiqueUniversité LavalCanada

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