# 臺灣博碩士論文加值系統

(44.220.44.148) 您好！臺灣時間：2024/06/21 14:54

:::

### 詳目顯示

:

• 被引用:0
• 點閱:161
• 評分:
• 下載:5
• 書目收藏:0
 從輸入單一模糊影像中去模糊已被長時間被討論著，大部分的單一影像去模糊演算法都使用了整張影像並且均使用了由粗略到精細的最大化似然機率的方法來估計點擴散函數，而這樣的方法通常需要很大的計算量和多次的疊代計算。我們觀察到利用整張影像來估計點擴散函數並不是永遠都能夠有好的效果，反而會使估計結果有錯誤並花更多時間計算。在這篇論文中，我們專注於加快演算法速度同時要能提升估計點擴散函數的正確率。我們提出了一個基於聯盟賽局的區塊選取方法，來選取對估計點擴散函數有幫助的區塊。我們一開始利用模糊影像的梯度資訊和一張擁有影像強結構資訊的圖來找出含有資訊的像素，然而單一像素的資訊是不足的。因此，以每個找到的像素當作中心點，我們皆形成一塊小區塊，目標就是從這些區塊中找出一些含有較高資訊的並結合在一起形成一個較大的區塊做為最後的結果。我們應用聯盟賽局來解決這個問題，在此聯盟賽局中，我們將每個區塊視為一個玩家，每個玩家試圖加入一個聯盟來改進自己的收益。我們設計聯盟的效益並利用夏普利值來公平分配每個玩家所得到的收益，賽局結束後，我們會得到一個聯盟使得所有玩家都擁有最滿意的分配，我們利用此聯盟來形成最後的區塊。我們的方法顯示對於真實模糊影像和合成的模糊影像皆能夠節省時間並且改善去模糊的品質。
 Deblurring from a single image has been extensively discussed. Most of single-image blind image deblurring methods using whole image to estimate the blur kernel based on a coarse-to-fine MAP approach and they are usually computational expensively due lots of iterations. We observed that using whole image to estimate blur kernel is not always a good option and may ruin the kernel estimation process but also need more computation time. In this paper, we focus on accelerating the blind deconvolution algorithm and increasing the accuracy of kernel estimation. We propose a coalition game based patch selection method to choose an informative patch for kernel estimation.We first find the informative pixels which is useful for kernel estimation using blur image gradient magnitude and a strong structure map. However, we consider that single pixel is not informative enough. For each pixel we found, we form a small patch centered at it. Our goal is to find a group of informative patch and united them into a large patch. We apply coalitional game to solve this problem. In our coalitional game, each patch represents a player, and they seek to join a coalition to improve their payoff. We design the utility for each coalition and compute the Shapley value to fairly distribute the utility to each player in the coalition. After the game, we will have a coalition such that no other player can obtain an outcome better than the current assignment and then, we use it to form our final patch. We show the speed-up and the quality improvement of our method both on real-world and synthetic images.
 Chapter 1 Introduction 5Chapter 2 Related Works 7Chapter 3 Patch Selection Method with Game Theory 93.1 Informative pixels selection 123.2 Coalitional game based patch selection 163.2.1 Ordered Coalitional game 19Chapter 4 Experimental Results 23Chapter 5 Conclusions 39Reference 40
 [1]Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006, July). Removing camera shake from a single photograph. In ACM Transactions on Graphics (TOG) (Vol. 25, No. 3, pp. 787-794). ACM.[2]Shan, Q., Jia, J., & Agarwala, A. (2008, August). High-quality motion deblurring from a single image. In ACM Transactions on Graphics (TOG) (Vol. 27, No. 3, p. 73). ACM.[3]Xu, L., & Jia, J. (2010). Two-phase kernel estimation for robust motion deblurring. In Computer Vision–ECCV 2010 (pp. 157-170). Springer Berlin Heidelberg.[4]Pan, J., Liu, R., Su, Z., & Gu, X. (2013). Kernel estimation from salient structure for robust motion deblurring. Signal Processing: Image Communication.[5]Krishnan, D., Tay, T., & Fergus, R. (2011, June). Blind deconvolution using a normalized sparsity measure. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 233-240). IEEE[6]Jia, J. (2007, June). Single image motion deblurring using transparency. InComputer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on (pp. 1-8). IEEE.[7]Bae, H., Fowlkes, C. C., & Chou, P. H. (2013). Patch mosaic for fast motion deblurring. In Computer Vision–ACCV 2012 (pp. 322-335). Springer Berlin Heidelberg.[8]Gupta, A., Joshi, N., Zitnick, C. L., Cohen, M., & Curless, B. (2010). Single image deblurring using motion density functions. In Computer Vision–ECCV 2010 (pp. 171-184). Springer Berlin Heidelberg.[9]Cho, S., & Lee, S. (2009, December). Fast motion deblurring. In ACM Transactions on Graphics (TOG) (Vol. 28, No. 5, p. 145). ACM.[10]Joshi, N., Szeliski, R., & Kriegman, D. J. (2008, June). PSF estimation using sharp edge prediction. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-8). IEEE.[11]Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., & Harmeling, S. (2012). Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database. In Computer Vision–ECCV 2012 (pp. 27-40). Springer Berlin Heidelberg.[12]Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2009, June). Understanding and evaluating blind deconvolution algorithms. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 1964-1971). IEEE.
 電子全文
 國圖紙本論文
 連結至畢業學校之論文網頁點我開啟連結註: 此連結為研究生畢業學校所提供，不一定有電子全文可供下載，若連結有誤，請點選上方之〝勘誤回報〞功能，我們會盡快修正，謝謝！
 推文當script無法執行時可按︰推文 網路書籤當script無法執行時可按︰網路書籤 推薦當script無法執行時可按︰推薦 評分當script無法執行時可按︰評分 引用網址當script無法執行時可按︰引用網址 轉寄當script無法執行時可按︰轉寄

 1 基於合作賽局針對提升影像去模糊效能的區塊選擇方法

 無相關期刊

 1 基於合作賽局針對提升影像去模糊效能的區塊選擇方法 2 基於合作賽局的多類影像共分割方法 3 利用邊緣偵測和賽局理論圖形切割的非監督式前景分割 4 針對單一影像盲去模糊的影像分解及區塊選取方法 5 基於合作賽局的機器學習式前景分割方法 6 利用多個先驗條件進行兩階段去模糊 7 基於影像金字塔以及區塊選擇的超解析度方法 8 基於雙邊濾波器與演化式賽局的超解析度方法 9 以區塊為基礎的影像去雜訊研究 10 基於階層式群集的非監督式影像共分割方法 11 運用標籤賽局於數位影像之主體重新對焦 12 利用文字影像雙色性質以及梯度性質的 去模糊方法 13 利用多標籤圖形切割的非監督式影像分割 14 基於文字影像高對比灰階性質的去模糊方法 15 使用邊緣偵測與基於路徑規劃的方法解決影像分割中需要使用者定義標籤的問題

 簡易查詢 | 進階查詢 | 熱門排行 | 我的研究室