本論文提出一基於優先權式信賴度傳遞的方法能夠修補缺失區域位在影像四周的情況。要能夠達到這個目的，本方法重新定義馬可夫隨機場中節點的產生方式，並且使用漸進式方法一層一層的去修補影像。同時為了降低其運算量，我們先限制取得候選區塊的範圍之後再使用亮度和飽和度來對馬可夫隨機場中之每個節點的候選區塊做篩選。最後為了產生一張視覺上美觀的結果圖，因此在修補的階段我們加入了節點的目標區塊和候選區塊間之相似度與信賴度值這兩個條件來判斷每個節點是否可以修補。
實驗結果顯示，不只缺失區域位在影像四周的修補結果，同時缺失區域位在影像中間的情況其修補的結果都令人滿意。

In this paper we propose a priority belief propagation based method to complete the missing region located on the periphery of the input image. To reach this goal, we redefine the nodes of Markov Random Field (MRF) and use onion-based approach to complete the image. Meanwhile, avoiding the heavy computation, we restrict the range to getting candidates for MRF. Then, applying intensity and saturation as criteria to filter the candidate of each node of MRF. Finally, we combine the similarity between MRF node’s target and best candidate patch and confidence value to decide whether the node is suitable to complete or not. From the experiments shows that our method successfully produces the visual plausible results.

目錄
第一章 緒論	1
第二章 相關文獻回顧	4
第三章 研究方法	10
3.1 priority BP與馬可夫隨機場(Markov Random Field)	13
3.2 Modified priority BP	20
3.2-1：定義MRF節點和取得候選區塊	20
3.2-2：計算基礎門檻值	22
3.2-3：候選區塊分群	23
3.2-4：Modified Priority BP	24
3.2-5：填補缺失區域的方法	27
第四章 實驗結果與討論	29
第五章 結論與未來研究方向	35
參考文獻	37
附錄：英文論文	39

圖目錄
圖 1 拼接產生的全景圖[8]	2
圖 2 為填補完成的全景圖(示意圖) [8]	2
圖 3 N. Komodakis et al. [10]的實驗結果	6
圖 4 對缺失區域在四周的影像使用priority BP的結果[11]	7
圖 5 J. Kopf et al. [11]的實驗結果	8
圖 6 K. He et al. [8]的流程示意圖	9
圖 7 系統流程圖	11
圖 8 待修補影像[11]	12
圖 9 遮罩影像(黑色代表缺失區域，白色代表已知)	12
圖 10 MRF的基本架構[10]	13
圖 11 Vp和Vpq的示意圖[10]	15
圖 12 節點接收和傳遞訊息的示意圖[10]	15
圖 13 priority BP演算法[10]	19
圖 14 圖中紅點為初始MRF節點	21
圖 15 圖中黃點為運作priority BP的節點	21
圖 16 圖中白點皆代表候選區塊左上角的座標	22
圖 17 target patch's intensity channel的示意圖（空白表示缺失點）	25
圖 18 target patch填入極值的示意圖	25
圖 19 填補缺失區域示意圖	28
圖 20 實驗結果1	31
圖 21 實驗結果2	32
圖 22 實驗結果3	33
圖 23 實驗結果4	33
圖 24 Initial patch size的影響	34
圖 25 confidence map的影響	34

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