臺灣博碩士論文加值系統

自動白平衡的目的在於讓使用者只須按下相機快門就能拍出一張色彩鮮明不失真的照片。此步驟為數位相機中影像處理重要的項目之一。若無白平衡的處理，在高色溫的光源下，所拍攝的照片將會偏藍，低色溫光源下所拍攝出來的照片將會偏紅。因此，自動白平衡必須能夠自動地校正色彩偏差的情形。一個擁有良好自動白平衡演算法的相機，所拍攝出來的照片顏色較令人討喜，且擁有讓人滿意的影像品質。
本文一開始先介紹自動白平衡的概念及其重要性，並詳細介紹各種不同的自動白平衡演算法，分別比較其優劣,並進一步提出一個新穎及穩定的演算法，利用色溫估計的方式完成自動白平衡。最後分別利用客觀及主觀的方式評鑑各種演算法。實驗結果顯示我們的方法擁有出色的結果以及符合多數人的喜好。

This thesis discusses automatic white balance in digital camera. Auto white balance is the most important image processing step in the image pipeline of digital camera, since it controls the color appearance of the image. Without auto white balance adjustment, the photo will become bluish under high color temperature and have reddish color cast under low color temperature. Therefore, a camera with good auto white balance method produces accurate and satisfactory color appearance, and good image quality.
In the beginning, we introduce what automatic white balance is and its importance. Then, we describe several previous methods and indicate these methods’ main drawbacks. Furthermore, we propose a novel and robust automatic white balance method by using color temperature estimation. Finally, we use objective and subjective evaluations to evaluate these methods. By experimental results, we can find that our method has robust and satisfactory results and is popular for most users.

Chapter 1 Introduction 1
1.1 Automatic White Balance in Image Pipeline 1
1.2 Color Temperature 2
1.3 Color Constancy 4
Chapter 2 Basic Method for Automatic White Balance 6
2.1 Gray World Assumption (GWA) 6
2.2 Specular Reflection Assumption (SRA) 9
Chapter 3 White Point Detection Method 13
3.1 White Object Purification 15
3.2 White Object Detection 17
3.3 White Balance Adjustment 18
Chapter 4 Color Temperature Estimation Method 25
4.1 Lin and Chen’s Method 27
4.2 Drawbacks of Lin and Chen’s Method 32
Chapter 5 Our Method 38
5.1 Preliminary Preparation 39
5.2 Gray Point Sieving and Color Temperature Scoring 48
5.3 Color Temperature Estimation and White Balance Adjustment 53
Chapter 6 Experimental Results 60
6.1 Simulation Methods 60
6.2 Test Images Selection 61
6.3 Evaluative Values of Simulations 73
6.4 Complexity 76
Chapter 7 Conclusions and Future Work 77
7.1 Conclusion 77
7.2 Future Work 78
References 79

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