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研究生： LE VIET HUNG
LE VIET HUNG
論文名稱： Deep Residual and Classified Neural Networks for Inverse Halftoning
Deep Residual and Classified Neural Networks for Inverse Halftoning
指導教授： 郭景明
Jing-Ming Guo
口試委員: 鍾國亮
Kuo-Liang Chung
楊傳凱
Chuan-Kai Yang
謝君偉
Jun-Wei Hsieh
蘇順豐
Shun-Feng Su
郭景明
Jing-Ming Guo
學位類別： 碩士
Master
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 72
中文關鍵詞： Inverse halftoning 、Halftoning 、Convolutional neural network 、Residual network 、Statistical analysis
外文關鍵詞： Inverse halftoning, Halftoning, Convolutional neural network, Residual network, Statistical analysis
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    • ABSTRACT i ACKNOWLEDGEMENTS ii CONTENTS iii List of Figures vi List of Tables ix List of Abbreviations x CHAPTER 1 - INTRODUCTION 1 1.1 Motivation and Problem Statement 1 1.2 Proposed Solution 2 1.3 Organization of Thesis 2 CHAPTER 2 - Digital Halftoning and Inverse Halftoning 4 2.1 Halftoning 4 2.1.1 Ordered Dithering 6 2.1.2 Error Diffusion 7 2.1.3 Dot Diffusion 9 2.1.4 Direct Binary Search 11 2.1.5 Halftone Types Comparison 12 2.2 Inverse Halftoning 13 2.2.1 A Naïve Approach 14 2.2.2 Look-up Table Method 15 2.2.3 Wavelet-based Method (WInHD) 16 2.2.4 Multiscale gradient estimator (FastIT) 16 2.2.5 Deep learning-based Method 17 CHAPTER 3 - Neural Networks 19 3.1 Machine Learning 19 3.2 Supervised Learning 19 3.3 Artificial Neural Networks 20 3.4 Multi-layer networks 20 3.5 Convolutional Neural Networks 21 3.6 Convolutional layer 22 3.7 Pooling layer 23 3.8 Fully-connected layer 23 3.9 Residual Learning and Skip Connection 23 3.10 Generative Adversarial Network 24 3.11 Internal Covariate Shift 25 CHAPTER 4 - – Proposed DRCNN Method 26 4.1 Network Architecture 26 4.1.1 Generator Network 26 4.1.2 Residual Blocks 32 4.1.3 Depth of network 32 4.2 Loss Functions 33 4.3 Statistical Analysis 36 4.4 Image Quality Assessments 40 4.5 Multi-tones Color Images 41 CHAPTER 5 - Experiments 42 5.1 Experimental Setup 42 5.2 Datasets 42 5.3 Hyper-parameters 43 5.4 Investigation of loss functions 44 5.5 Investigation of perceptual loss at different convolutional layers 45 5.6 Performance on different variance models 46 5.7 Experimental Results 47 5.7 Future Works 52 CHAPTER 6 - Conclusion 55 BIBLIOGRAPHY 56

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