臺灣博碩士論文加值系統

在本篇論文中，我們提出二類型虛擬印表機的模型去實現實際印表機的品質。第一類型是利用光電成像的技術且利用實際印表機的參數建構而成，但是此類型的虛擬印表機印列影像時會耗費許多的時間。因為耗費時間的因素，讓我們想要改進虛擬印表機使得速度更快。根據漢默斯坦(Hammerstein/Wiener)的技術可以同時掌控虛擬印表機的線性區塊與非線性區塊，再配合上模糊類神經網路(Fuzzy Neural Network)可以讓虛擬印表機印列功能更佳的完善。因此我們第二類型的虛擬印表機即是用Hammerstein/Wiener 概念結合Fuzzy Neural Network技術而成。
我們知道實際印表機在印列影像前都必須將灰階或是彩色的影像先轉換成二元的影像才能夠進行列印。然而把灰階影像轉換成二元影像的技術稱為半色調技術，在本篇論文中會簡單的介紹現今流行的半色調技術並提出新的結構。此新結構能夠含蓋現今流行的半色調技術與Model-Based技術結合，利用此模型經印表機列印出來的品質會比傳統半色調經印表機列印出來的品質更佳。

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. In this thesis, we can simple introduce recent popular halftone algorithms, and propose new structure, the structure could include all of recent popular halftone algorithms and associate with Model-Based method. Using this model passed a printer printed quality better than traditionally halftone went through a printer.

Acknowledgement i
Chinese Abstract ii
English Abstract iii
Contents iv
List of Tables vi
List of Figures vii

Chapter 1 Introduction 1
1.1 Motivation 4
1.2 Literature Review 8
1.2.1 Human Visual System 11
1.2.2 Existing Halftoning Algorithms 14
1.2.2.1 Bayer Dither Method 15
1.2.2.2 Error Diffusion 17
1.2.2.3 Dot Diffusion 19
1.2.2.4 Direct Binary Search 21
1.2.2.5 Model-Based Methods 22
1.2.3 Linear Filtering in Spatial Domain 23
1.2.4 Fuzzy Neural Networks 26
1.2.5 Hammerstein/Wiener Models for Nonlinear System 29
1.3 Organization and Contribution 30
Chapter 2 System Description 32
2.1 Electrophotographic Process 33
2.2 Modulation Transfer Function of a Printing System 38
2.3 Design Framework of Model-based Halftoning Algorithms 45
Chapter 3 Incorporating a Virtual Printer into Digital Halftoning 48
3.1 Model-Based Halftoning with a Virtual Printer 48
3.2 Mathematical Model of a Virtual Printer 51
3.3 Experimental Results 53
3.4 Conclusions 60
Chapter 4 Incorporating the Hammerstein Model of a Laser Printer into Digital Halftoning 62
4.1 Configuration of the Hammerstein Model with Fuzzy Neural Network 62
4.2 Parametric Training of the Hammerstein Model 84
4.3 Evaluation the Hammerstein Printer Model 93
4.4 Experimental Results 98
4.5 Conclusions 113
Chapter 5 Conclusions and Future Work 115
5.1 Conclusions 115
5.2 Future Work 116
References 117
Appendix A Parameters for the Virtual Printer 130
Appendix B Numerical Approximation of Partial Differential Equations 135
Appendix C Training Samples for the Fuzzy Neural Network 140

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