Halftone Algorithm Development Based on a Virtual Printing System and its Hammerstein Model with Application to a Class of Monochrome Laser Printers

In this thesis, we propose two kinds of virtual printer models to achieve real printers’ quality. First kind uses eletrophotography technology and applies real popular printers’ parameters to achieve, but these kinds of virtual printers printed images always cost much time. Because this reason, let we want to improve these kinds of printers to print faster. According to technology of Hammerstein/Wiener, we could control not only linear block but also nonlinear block on virtual printers and then associate with fuzzy neural network. Thus the virtual printers printed performance better than before. Therefore our second kind of virtual printer is applied technology of Hammerstein/Wiener concept associate with fuzzy neural network. As we know, real printers printed before must to be converting gray scale images or color images into the binary images, and the binary images are able to print. However the method of convert gray scale images into binary images is called halftone algorithm. 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