Resolutionable cellular neural networks

This paper describes resolutionable cellular neural networks (CNNs) by which a wide range of image applications can be done based on spatio-temporal dynamics to generate triplet (red, green, blue) combinational secondary color for a full color input with any resolution. Area intensity of the secondary color can be generated by using local dynamics of inner cells in each pixel and color image processing can be done by using global CNN dynamics for the secondary color outputs and full color inputs. We use multilevel discrete time cellular neural network to perform its local and global dynamics. The templates are designed by the minimization of Lyapunov-energy function to minimize the least-square distortion for digital still/moving images. High quality of the digital images was demonstrated by using Canon ferroelectric liquid crystal display