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研究生:張家暐
研究生(外文):Chang, Chiawei
論文名稱:以多層次深度推論及光源估測進行單張影像除霧
論文名稱(外文):Single Image Defogging by Multi-level Depth Inference and Airlight Estimation
指導教授:王元凱王元凱引用關係
指導教授(外文):Wang, Yuankai
口試委員:張陽郎陳良華
口試委員(外文):Chang, YanglangChen, Lianghua
口試日期:2013-01-08
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:90
中文關鍵詞:馬爾可夫隨機場影像強化除霧除霾深度空氣光
外文關鍵詞:Markov random fieldImage enhancementDefoggingDehazingDepthAirlight
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本篇論文提出一個單張影像除霧演算法。還原一張有霧影像必須具備正確的深度影像,該張深度影像以多層次深度推論的方法取得,首先將不同補丁大小的暗通道影像融合為一張初估的深度影像,接著由馬爾可夫隨機場施加標籤值至初估的深度影像的鄰接區域的深度,計算鄰接區域的深度機率以補償深度的高估或低估,進而得到一張正確的深度影像。單張影像除霧除了需要深度影像,還需要空氣光,稱之為光源。基於物理學的假設,光源自天空而來,因此利用深度影像找尋天空的位置,其中天空視為深度影像中最深且面積最大的集合。正確的光源估測可以幫助影像還原結果得到較好的可見度與較好的對比度。本演算法對多張有霧影像與有霾影像進行實驗,實驗結果證實本演算法透過正確的深度推論以及估測光源可以還原而得高品質的影像。
This paper presents an automatic method for the defogging from a single haze image. To recover a foggy image, an accurate depth map is estimated from a multi-level inference method, which fuses depth maps with different sizes of patches by dark channel prior. Markov random field (MRF) is applied to label the depth level in adjacent region for the compensation of wrong estimated regions. Airlight is automatically estimated as the deepest and largest area from the MRF labeled result. The accurate estimation of airlight provides good restoration with respect to visibility and contrast but without oversaturating. The algorithm is verified by a handful of foggy and hazy images. Experimental results demonstrate that the defogging method can recover high-quality images through accurate estimation of depth map and airlight.
Abstract(in Chinese) i
Abstract ii
Acknowledgement(in Chinese) iii
Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
Chapter 2 Related Works 5
2.1 Physics-based Image Degradation Model 5
2.2 Previous works 7
Chapter 3 Inference of Depth Map 11
Chapter 4 Airlight Estimation 39
Chapter 5 Experimental Results 55
Chapter 6 Conclusion and Future Work 75
References 76

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