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研究生：	李軒 Li, Hsuan
論文名稱：	基於黑暗通道的改良除霧演算法之研究 A Study on Improving the Dark Channel-Based Dehazing Algorithm
指導教授：	戴顯權 Tai, Shen-Chuan
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	56
中文關鍵詞：	除霧、黑暗通道、大氣光、色偏、光暈效應
外文關鍵詞：	dehazing, dark channel, atmospheric light, color cast, halo artifact
相關次數：	點閱：67 下載：1
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在戶外拍攝的影像通常因為大氣中的介質散射環境光而造成影像品質下降，特別是有霧氣存在的時候。而有霧的影像不只會遺失許多細節並且不利於許多的應用，例如監視系統、智能汽車和物體辨識。所幸，近年來已經提出許多單張影像除霧的技術。在這篇論文我們提出基於黑暗通道的改良除霧演算法。首先，提出的演算法平均較亮區域像素數值做為大氣光顏色向量，同時分析霧圖的亮度分布以取得大氣光強度。結合上述的向量與強度可以使得處理後的影像沒有色偏及黯淡等問題。此外，經由去除粗糙的傳輸地圖中多餘的高強度像素，可以有效地減少光暈效應。最後演算法引入了指數權重來加強背景除霧，可使影像細節更加地清楚。實驗結果顯示，我們的結果比現在主要的除霧演算法結果還要好。

Images of outdoor scenes are usually degraded because the ambient light is scattered, especially in haze. Many details will be lost in hazy images and these images are inappropriate to be used in many applications such as surveillance, intelligent vehicles and object recognition. Fortunately, single image haze removal methods have been proposed recently. In this thesis, an improved single image dehazing algorithm is proposed which is based on dark channel. First, the proposed algorithm determines the color vector of the atmospheric light after averaging the brightest pixels. And it analyzes the brightness distribution of the hazy image to estimate the intensity of the atmospheric light simultaneously. As a result, it can avoid color cast and dim phenomenon in output images. Furthermore, it also removes the redundant high value pixels of the coarse transmission map to reduce the halo artifact. Finally, the proposed algorithm introduces an exponential weight when recovering the scene radiance, so the details of image can be clearer.
The experimental results show that it achieves as good or even better results compared to the main present-day algorithms as illustrated.

Contents.....................................................................ii
List of Figures..............................................................iv
Chapter 1 Introduction.......................................................1
Chapter 2 Background.........................................................3
2.1 Haze Imaging Model.......................................................3
2.2 Single Image Dehazing....................................................5
2.2.1 Dark Channel Prior.....................................................5
2.2.2 Estimating the Transmission............................................7
2.2.3 Estimating the Atmospheric Light.......................................8
2.2.4 Guided Image Filtering.................................................10
2.2.4 Recovering the Scene Radiance..........................................16
2.3 The Analysis of Previous Works on Contrast...............................17
2.3.1 Tan’s Method..........................................................17
2.3.2 Jin’s Method..........................................................19
Chapter 3 The Proposed Algorithm.............................................21
3.1 Estimating the color vector and the intensity of the atmospheric light...23
3.2 Reducing halo artifact...................................................31
3.3 Recovering the scene radiance............................................37
Chapter 4 Experimental Results...............................................41
4.1 Comparison with Other Approachs..........................................41
4.2 More Results.............................................................45
Chapter 5 Conclusion and Future Work.........................................53
5.1 Conclusion...............................................................53
5.2 Future Work..............................................................54
Bibliography.................................................................55
                                    

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校內：2014-07-23公開
校外：2018-07-23公開

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