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研究生:林韶華
研究生(外文):Shao-Hua Lin
論文名稱:數位影像擷取裝置內建模糊推理與類神經網路之高效能自動白平衡演算法設計
論文名稱(外文):Design of High-Performance Automatic White Balance Algorithms with Inherent Fuzzy Inference and Neural Networks for Digital Image Capture Devices
指導教授:陳正倫陳正倫引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:70
中文關鍵詞:白平衡模糊系統單一目標最佳化方法模糊類神經網路
外文關鍵詞:White balancefuzzy systemsingle objective optimizationfuzzy neural network
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自動白平衡在數位相機中扮演著很重要的角色。缺少自動白平衡,在低色溫下影像會偏向紅色,在高色溫下影像會偏向藍色。本篇論文提出兩種白平衡方法:最佳化方法及色溫估測法。最佳化方法將白平衡化為最佳化問題並解決;色溫估測法利用模糊類神經網路估測未知影像之色溫進而對該影像做修正,經實驗後發現這兩種方法都得到不錯的結果。
Auto white balance is an important process in digital camera or CCD camera. Without auto white balance, the image becomes reddish under low color temperature and becomes bluish under high color temperature. In this thesis, we propose optimization methods and light estimation method. The optimization methods formulate the auto white balance to three optimization problems and solve them. The light estimation method use two steps: (1) utilize fuzzy neural network to estimate the color temperature of an image; (2) apply scale factors to adjust the image. Our methods have good performance in the experiment.
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 1
1.2 Literature Review 2
1.2.1 Gray World Method (GWM) [1]-[8] 2
1.2.1.1 Traditional Gray World Method 2
1.2.1.2 Lam’s Method [3] 3
1.2.2 Perfect Reflector Method (PRM) [9] 4
1.2.3 Light Estimation Method [10]-[17] 5
1.2.3.1 Wang’s Method 5
1.2.3.2 Huo’s Method 6
1.2.4 Chikane’s Method [18][30] 7
1.3 Organization and Contribution 7
Chapter 2 System Description 9
2.1 Color Temperature [31] 9
2.2 Color Space 9
2.3 Experimental Equipment 10
Chapter 3 The Optimization Based on Gray World Assumption 12
3.1 Automatic White Balance Formulated as an Optimization Problem 13
3.1.1 Formulation of the Optimization Problem 13
3.1.2 Fuzzy Rule Inference 16
3.1.3 Update Law for Decision Variables 23
3.2 Experimental Results 26
3.3 Conclusion 35
Chapter 4 Light Estimation Method with Fuzzy Neural Network (FNN) 36
4.1 Overview of FNN [34]-[37] 37
4.2 Training by FNN [37] 40
4.3 White Balance Adjustment 46
4.4 Experimental Results 47
4.5 Conclusion 64
Chapter 5 Conclusions and Future Work 65
5.1 Conclusions 65
5.2 Future Work 65
References 66
Appendix A 71
Appendix B 73
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