臺灣博碩士論文加值系統

由於科技的進步，數位相機暨智慧型手機之攝影設備越來越普遍，人們可以更容易地透過這些設備，取得圖像資訊。近幾年來，複合式圖片的應用也隨著技術的進步，悄悄地在世上廣泛的流行，例如：高動態範圍成像就是一個廣為人知的應用。當人們使用擁有高動態範圍成像功能的相機拍攝影像時，相機會拍攝多張不同曝光度的照片，並且融合成一張擁有場景中所有細節的圖片。在相似的概念延伸下，我們提出兩種新穎的影像合成演算法來解決背景擷取與多重焦距影像合成的兩個問題。本論文第一部分會介紹如何利用隨機漫步模型從影片中取得背景圖片；第二部分會介紹如何使用聯合高斯條件隨機場從不同焦距的圖片中得到清楚對焦的圖片。
第一部分，一般而言背景擷取是假設存在一張乾淨的背景圖片在輸入的影片當中，但事實上，許多情況可能違反這個假設，例如高速公路交通影片。因此，我們提出以機率模型的方式來合成影片中的候選背景區塊，並將這個方法導向於隨機漫步的問題，並基於時間和空間的關係尋求全域最佳解。此外，我們還設計了兩個品質量測來考慮像素之間的空間和時間一致性和對比清晰度作為背景像素選擇的基礎。在幀之間靜態背景具有高時間一致性，因此，我們利用時間對比度濾波器和光流法擷取出無動態的區塊來提高我們合成的準確度。實驗結果顯與，我們的演算與過往最佳的演算法比較，可以成功的擷取出無瑕疵的背景圖片，並且有著更低的計算複雜度。
第二部分，一般由於光學鏡頭先天上的限制，在拍攝照片時不可能將所有物體都對於焦距內，而多焦距影像合成演算法可以用於解決這個問題。然而在真實世界的場景中，不只包含靜態物體也包含移動物體。大多數現有的演算法提供對於靜態場景好的解決辦法，但這不包含場景內存在移動的物體，現有的演算法對於場景內存在移動物體所處理的結果都會產生出類似鬼影的現象。為了解決這個問題，我們提出新的多焦距影像合成演算法用於動態場景上。首先，我們的演算法會使用動態場景分析來偵測移動物體，接著我們設計了兩個特徵值，對焦的特徵值以及連續性的特徵值來量測輸入的多焦距影像，並將多焦距影像合成問題導向進聯合高斯條件隨機場模型。最後，採用最大後驗（MAP）來計算每個輸入圖像的像素的權重，並將輸入圖像融合成全聚焦圖像。實驗結果顯示所提出的演算法在靜態和動態場景都比過往的演算法來的優異。

Thanks to new technological advances, digital cameras and smart phone cameras become more and more popular. People can more easily obtain various image information through these devices. The multiple image technologies have been prevailed in the world. For example, the high dynamic range imaging (HDRI) is a well-known application by which a user is able to take multi-exposure photos and fuse these images into one single HDR image that provides all the details in a scene. Inspired by this concept, we proposed two novel image fusion algorithms on background extraction and multi-focus image fusion. In this work, we will first describe how we utilize random walk model to extract a clean background image from video. Secondly, we will introduce how we employ joint Gaussian conditional random fields model to obtain an all-in-focus merged image from multi-focus images.
In the first part, generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship. Furthermore, we also design two quality measures to consider spatial and temporal coherence and contrast distinctness among pixels as background selection basis. A static background should have high temporal coherence among frames, and thus, we improve our fusion precision with a temporal contrast filter and an optical-flow-based motion-less patch extractor. Experiments demonstrate that our algorithm can successfully extract artifact-free background images with low computational cost while comparing to state-of-the-art algorithms.
In the second part, generally, due to the limited depth-of-field of optical lenses, it is usually impossible to obtain an image which all relevant objects are in focus. To overcome this problem, it can be solved by multi-focus image fusion. However, the scene of the real world is not only static objects but also moving object. Most of the existing algorithms can solve the multi-focus image fusion problem that do not contain any moving objects. The existing algorithms result in ghosting artifact by the presence of moving objects. To solve this issue, we proposed a novel multi-focus image fusion algorithm for dynamic scenes. First, our algorithm detects the moving objects by dynamic scene analysis. Second, we design two features, focus-feature and consistency-feature, to evaluate each input image. Then, we formulate the multi-focus image fusion problem into a joint Gaussian conditional random fields model. Finally, we use the maximum a posteriori (MAP) to compute the weight of each pixel in each input image to fuse an all-in-focus image. Experimental results show that the proposed method outperforms the state-of-the-art methods for both static and dynamic scenes.

Abstract in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii
List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
1 Background Extraction Using Random Walk Image Fusion . . . . . . . . . . . 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.2 Random Walks for Background Extraction . . . . . . . . . . . . 9
1.3.3 Quality Measure Function . . . . . . . . . . . . . . . . . . . . . 12
1.3.4 Algorithm Summarization . . . . . . . . . . . . . . . . . . . . . 15
1.4 Optical Flow Motion-less Acceleration . . . . . . . . . . . . . . . . . . . 15
1.5 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . . 18
1.5.1 Video Sequences and Ground Truth . . . . . . . . . . . . . . . . 18
1.5.2 Analysis of Free Parameters . . . . . . . . . . . . . . . . . . . . 20
1.5.3 Results and Comparisons . . . . . . . . . . . . . . . . . . . . . . 21
1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2 Multi-Focus Image Fusion Based on Joint Gaussian Conditional Random Fields 46
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.2 Moving Object Detection . . . . . . . . . . . . . . . . . . . . . . 51
2.2.3 Focus-Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.2.4 Consistency-Feature . . . . . . . . . . . . . . . . . . . . . . . . 54
2.2.5 Joint Gaussian Conditional Random Field for Multi-Focus Image
Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.2.6 Weight of Multi-Focus Image . . . . . . . . . . . . . . . . . . . 58
2.3 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.3.1 Performance Evaluation for Static Scenes . . . . . . . . . . . . . 60
2.3.2 Performance Evaluation for Dynamic Scenes . . . . . . . . . . . 82
2.3.3 Evaluation of Computational Complexity . . . . . . . . . . . . . 100
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

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