臺灣博碩士論文加值系統

自從第一台光場相機產品在2012年被lytro推出後，光場相機的研究以及應用逐漸在增加，光場相機跟傳統相機的差異主要是在光場相機能夠捕捉到場景中的角度資訊，並且能夠調整角度資訊以及位置資訊的取捨。藉由使用光場相機，單次拍照所能獲得的資訊變得更加豐富。藉由使用這些資訊，我們可以重建場景的深度以及產生不同角度的影像。雖然如此，但深度重建以及產生影像的問題比傳統相機來的複雜，因為光場相機是鏡片陣列的組合，且每個鏡片後都會產生一個子影像。另外如果我們想要精準的重組一張影像，我們必須獲得每張子影像的偏移輛，因此必須先獲得深度的資訊。. 光場相機的子影像的重組問題與場景中的資訊有密切的關係。我們必須同時或者接連的處理子影像的重組問題以及深度重建問題。在這篇論文中，我們會先用迴歸分析先取得子影像的關係。然後我們會用立體匹配演算法來獲得場景中的深度，以及使用影像繪圖法來增加重建影像的品質。另外我們使用四元樹以及白場的影像來增進我們演算法的效果。最後，我們會比較我們所提出的方法以及之前的光場相機的子影像重建方法以及深度重建方法，並指出我們的方法擁有較好的結果

After the first commercial hand-held plenoptic camera was presented by Ng in 2012, the applications and the research of plenoptic cameras were getting richer in recent years. The major difference between the plenoptic camera and the traditional camera is that the plenoptic camera can capture the angular information in the scene and adjust the tradeoff between the spatial resolution and the angular information. With the use of the plenoptic camera, the information we get from a single shot of a camera is enriched. By using the information, we can reconstruct the depth of scene and render an image in different views. Nonetheless, the depth reconstruction and the rendering problems are more complicated than those of the traditional camera, since the plenoptic camera is consisted of a lens array and each lens leads to a micro image. In addition, if we want to render the image precisely, we have to obtain the disparity of each microimage pair and hence the depth information first. Because the rendering problem of the plenoptic camera is closely related to the depth of scene, we have to handle the rendering problem and the depth reconstruction problem at the same time or in sequence. In this thesis, we first obtain the relationship among microlenses by using regression analysis. Then, we use the stereo matching technique to get the depth of scene and the image-based rendering technique to improve the quality of the reconstructed image. Besides, we use quad-tree and white image to improve the performance of proposed method. In the end, we compare the result of the proposed algorithm with the previous work for rendering the microimages acquired from the plenoptic camera rendering and depth reconstruction and show that the proposed algorithm has better performance.

口試委員會審定書 #
誌謝 i
中文摘要 iii
ABSTRACT v
CONTENTS vii
LIST OF FIGURES ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Organization 7
Chapter 2 Experiment data, blending methods and Implementation in basic rendering 8
2.1 Focused plenoptic camera rendering 8
2.1.1 The implementation of basic rendering 10
2.2 Pyramid blending 12
2.3 Poisson blending 16
2.4 Gradient based image stitching (GIST) 19
2.5 Discussion 21
Chapter 3 The All in-focus Rendering and the Adjusted
Panorama algorithm 23
3.1 Introduction 23
3.2 Introduction to stereo matching methods 31
3.3 Kolmogorov and Zabih’s Graph Cuts Stereo Matching 32
3.3.1 The energy function 32
3.3.2 Expansion Move 34
3.4 Adaptive Normalized Cross-Correlation method 37
3.5 Panorama method using stereo matching 42
3.6 Simulation results and Discussion 44
Chapter 4 Proposed rendering and disparity-finding method 46
4.1 The implementation of proposed method 46
4.2 Simulation result and discussion 50
Chapter 5 Conclusions and Future Work 52
5.1 Conclusions 52
5.2 Future Work 52
REFERENCE 54
PUBLICATION 58

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