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4 April 2001 Image coding by cellular neural networks
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We present the pyramidal wavelet coder implemented with a Cellular Neural Network architecture, as an example of a Cellular Neural Network application, considering that some times it is extremely desired the massive and real-time processing and this kind of architecture fits very well for such purposes. The pyramidal wavelet coder works performing the image wavelet transform plus threshold and quantization operations. The wavelet transform consists essentially in a bank of filters, where an image is passed through them repeatedly, and after each filtering a sampling operation is performed. Once image has been filtered and sampled according the rules of the pyramidal image coder, the threshold operation is carried out, where we pretend to keep only the most significant wavelet coefficients. Finally, a quantization operation is performed in order to translate the coefficient values to a discrete environment.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rodrigo Montufar-Chaveznava, Domingo Guinea, Maria C. Garcia-Alegre, and Victor M. Preciado "Image coding by cellular neural networks", Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001);

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