Conversion of Resolution and Intensity using Cellular Neural Networks

The spatio-temporal dynamics by CNN(Cellular Neural Network) must generate nonlinear interpolative effects which have not been generated by a conventional image processing by sequential machine. In this research, a high quality secondary color image can be generated by using the CNN spatio-temporal dynamics for any resolution for both of a dense gradation image and an area intensity image. The dense gradation image is got from the area intensity image generated from the spatio-temporal dynamics of the CNN.Each cell of the CNN controls on-off state of the corresponding area element in pixel and then the intensity of each pixel can be controlled by changing the number of area elements per each pixel. That is, multi-value intensity can be generated by only using a set of 1-bit cells in the universal CNN.The number of bits per pixel for color density should be controlled efficiently by the number of pixels for a given input resolution because high density is required for low resolution and low density is enough for high resolution. This auto-resolution is very useful from practical viewpoints. The CNN can be used efficiently for the auto-resolotion because the number of inner cells in each pixel is controlled automatically, even if the number of quantized levels of inner cell is only 2 (binary). The problem is how the color density per pixel can be generated by using spatio-temporal dynamics by a set of inner cells in each pixel. The problem can be solved based on a conversion from spatial area intensity process to density process in each pixel. The CNN for auto-resolotion has hierarchical structure in which a global CNN consists of cells corresponding to the number of pixels in an image and a local CNN consists of real inner cells. The discrete time CNN has been designed as a chip by using Parthenon high level language developed by NTT.