# 臺灣博碩士論文加值系統

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 動態模糊(Motion blur)是現在數位攝影拍攝中常遇到的問題，通常發生於光線不足的情況相機與移動物體的相對運動所造成的。因此如何有效解決動態模糊的問題也成為主要的研究方向之一。影像去模糊(Image deblurring)在近幾年來在電腦視覺與影像處理領域上一直是重要的發展議題之一。大部分影像去模糊演算法是使用整張影像去估計出點擴散函數(Point Spread Function)，再利用點擴散函數進行反卷積(Deconvolution)運算所產生的結果，使用整張影像去估計點擴散函數常常會造成相當大量的計算時間以及複雜的運算，並且這種方法得出的去模糊影像不一定會有最佳的結果，影像中許多部份更可能會使得估計的點擴散函數產生估計誤差。為了防止這個問題，這本篇論文中我們提出一個基於大階梯邊緣(Large Step Edge)的影像區塊選擇法選出對於估計點擴散函數有用的區塊，首先我們先找出影像中大階梯邊緣的區塊，並利用這些區塊當作後選區，從這些候選區中計算影像中的梯度值，找出具有強邊緣且包含大階梯邊緣的區塊來做為估計點擴散函數的區塊，從實驗結果顯示我們的方法在模糊影像的點擴散函數估計和還原後的結果都能有良好的結果，並且能夠節省去模糊的時間以及獲得一張還原清晰的去模糊影像。
 Motion blur is common problem in the field of digital photographing, usually happened in situations of insufficient light and relative movement of target object. It’s a main research way that how to solve the motion blur problem effect.Image deblurring is a main development issue in the field of computer vision and image processing for the recent year. The common way of deblurring algorithm is that using a whole image to estimate the point spread function. Then using it to process deconvolution operation to generate the result. However, using the whole image to estimate the point spread function will cost a huge computing time and complex computing, and the result of deblurring image this way may not be the best one. Lots of the part in the image may cause inaccuracy of the point spread function.To prevent the problem, we propose an image sector selecting function based on large step edge to select useful sector for estimating the point spread function. First, we find out the large and medium step edge sector of the image, and we mark this sector as second selecting sector. The finding out the strong edge including sector of large step edge as sector of estimating the point spread function based on the gradient value of these waiting selecting sector of computing image. Our method on blurring image of the point spread function cause good result on estimating and restoring, and it reduces the time of deblurring and getting a clear restored deblurring image.
 第一章 緒論 11.1 研究背景及目的 11.2 論文架構 4第二章 文獻探討 52.1 基於影像梯度的模糊影像區塊選擇法 52.2 自動調整影像梯度的模糊影像區塊選擇法 72.3 結合二階微分與影像梯度的模糊影像區塊選擇法 9第三章 研究方法 113.1 取得影像中之大階梯邊緣 123.2 影像梯度計算與區塊選擇法 143.3點擴散函數的估計 153.4反卷積(DECONVOLUTION)影像還原計算 16第四章 實驗結果及討論 19第五章 結論與未來展望 44參考文獻 45
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 1 使用焦距分佈圖偵測演算法與適應性反卷積於單張影像非一致性去模糊之研究 2 一個使用區域極值濾波器之改良Richardson-Lucy單張影像去模糊演算法

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