Depth-Image-Based Rendering(DIBR) 是立體影像合成，最常被使用的方法，這個方法是根據一張影像及其深度的資訊，合成出不同視角的影像。DIBR包括兩個主要的運算 : warping及hole_filling。利用影像的深度資訊，觀賞者所在的位置，以及螢幕的資訊，就可以計算出影像中各個物體的位移量。Hole_filling的部分，許多研究提出各自己的方法，但合成出來的影像仍然有一些瑕疵。本篇論文提出一個依據破洞附近的圖像資訊，改善物件邊緣補洞不良的情況。最後本論文有一些實驗的結果，說明我們的方法可以在物件邊緣的附近，產生出更自然的形狀，以及硬體的面積與執行效能的數據。

Depth-Based Image Rendering (DIBR) is a popular method to generate 3D virtual image at different view positions using an image and a depth map. In general, DIBR consists of two major operations: image warping and hole filling. Image warping calculates the disparity from the depth map given some information of viewers and display screen. Hole filling is to calculate the color of pixel locations that do not correspond to any pixels in the original image after image warping. Although there are many different hole filling methods that determine the colors of the blank pixels, some undesirable artifacts are still observed in the synthesized virtual image. In this thesis, we present an approach that examines the geometry information near the region of blank pixels in order to reduce the artifacts near the edges of objects. Experimental results show that the proposed design can generate more natural shape around the edges of objects at the cost of more hardware and computation time.

第1章 概論 1
研究動機 1
貢獻 1
本文大綱 1
第2章 3D視覺基本原理 2
2.1 3D立體成像介紹 2
2.1.1立體視覺成因 2
2.1.2視差 4
2.2 3D立體顯示技術之研究與發展 6
2.2.1裸眼式 6
2.2.2戴眼鏡式 8
第3章 DIBR原理及相關研究 11
3.1 Warping 11
3.2 Hole Filling 16
3.3 Depth-map filter 18
第4章 硬體實作流程 20
4.1 單一影像合成之硬體架構說明 20
4.1.1Warping With ROM Table 21
4.1.2Warping Without ROM Table 24
4.1.3兩種架構之比較 26
4.2 延伸呈像範圍 27
4.3 多視角影像合成之硬體架構說明 31
第5章 軌跡補洞法 33
5.1 軌跡補洞動機 33
5.2 軌跡補洞流程及演算法 36
5.2.1第一版 44
5.2.2第二版 48
5.2.3第三版 54
第6章 結果與改進要點 59
6.1 實驗結果1 59
6.2 實驗結果2 63
6.3未來展望 71
參考文獻 72

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