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.

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