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研究生:李丞祐
研究生(外文):Chen-Yu Lee
論文名稱:基於色彩空間的影像偵測與強化技術
論文名稱(外文):Color Space-based Digital Image Detection and Enhancement
指導教授:貝蘇章
指導教授(外文):Soo-Chang Pei
口試委員:吳家麟徐忠枝李枝宏曾建誠
口試委員(外文):Ja-Ling WuJong-Jy ShyuJu-Hong LeeChien-Cheng Tseng
口試日期:2013-06-08
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:97
中文關鍵詞:膚色偵測水下影像增強除雨除雪
外文關鍵詞:Skin color detectionUnderwater image enhancementRain/snow removal
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在數位影像拍攝及處理的過程中,總是存在著許多問題。如拍攝人像時,需抓取人臉的位置以此提升鏡頭在對焦上的準確度。又如在水下攝影時,光線經過水體的衰減後,環境光的白光中,除了藍光外都有大幅度的衰減,進而使的影像失去其在白熾光下拍攝所應有的顏色。而在雨中與雪中攝影時,則會遇到雨水及雪在快門曝光的時間內,快速通過拍攝範圍內,進而造成影像中有細條狀雜訊的問題。這些問題解決時方法的好壞,會嚴重影響到運算的成本與時間,因此,個個問題都需要一套快速而有效的演算法來解決。
本篇論文中,分別探討膚色偵測、水下影像增強以及雨與雪景的移除。在膚色偵測的部分,將分別介紹在不同的色彩空間中的偵測方式[1]-[6],並提出一套基於影像正規劃所設計的膚色偵測方式。而在水下影像增強方面則是介紹基於色彩模型[7]與波長補償模型[8]建立的水下影像處理方式,並提出了一套基於色彩轉換[17]與dark channel prior-based [10], [11]的新的處理方式。最後則是單張影像去雨與除雪,其中介紹了guided filter [19], [21], [29]的去雨及除雪的方式,並提出了一種基於頻譜分析[20]與物件辨識與移除的影像處理方式。


In the procedure of capturing and processing a photo, there are always a lot of problems in the course. For example, when we want to take a picture for someone, we usually want to focus on the people. Finding the position of the faces in the photo by features such as skin color will play an important role in the procedure. At the time taking a photo in the water, we will find that the ambient light becomes blue instead of white. The intensity of the photos taken in the water will have the highest average value in the blue plate. When we take a picture in the raining or snowing day, the photographs are disturbed by the rain or snow streaks which appear in the range of the scene. It will let the photographs having a lot of steak-shape noise. The methods to solve these kinds of problems will severely affect the time consumption.
We will discuss the methods of skin-color detection, underwater image enhancement and rain/snow removal in single image in this thesis. In the part of skin color detection, we will introduce the methods of detecting the skin color in different color spaces [1]-[6]. Besides, we propose a new method for skin color detection which is based on image normalization. In the part of underwater image enhancement, we introduce two methods to fix the photograph. One of the method is based on the colour model [7] and the other is based on wavelength compensation and dehazing [8]. Besides, we also propose a method based on color transfer [17] and dark channel prior-based [10], [11]. In the part of rain/snow removal in single image, we will introduce the methods based on guided filter [19], [21], [29]. We propose a new method based on frequency analysis [20] and object recognition to solve the problem of rain and snow in the photograph.


口試委員會審定書 #
致謝 i
中文摘要 ii
Abstract iii
CONTENTS v
LIST OF FIGURES x
Chapter 1 Introduction 1
Chapter 2 Related Work of Skin Color Detection 5
2.1 Introduction 5
2.2 Color Balance 5
2.3 Color Space used for Skin Modeling 9
2.3.1 RGB 9
2.3.2 Normalized RGB 11
2.3.3 HSV 12
2.3.4 TSL 14
2.3.5 YCbCr 15
2.4 Skin Modeling 16
2.4.1 RGB 16
2.4.2 YCbCr 18
2.4.3 Normalized RGB 20
Chapter 3 Proposed Method of Skin Color Detection 23
3.1 Introduction 23
3.2 Proposed Method 24
3.2.1 Define Skin Region 24
3.2.2 Skin Color Normalization 27
3.2.3 Re-define Skin Region 28
3.3 Experiment Results 29
3.4 Conclusions 32
Chapter 4 Related Work of Underwater Image Enhancement 33
4.1 Introduction 33
4.2 Colour Model 35
4.2.1 Contrast Stretching 36
4.2.2 Color Correction 38
4.3 Wavelength Compensation and Dehazing 40
4.3.1 Underwater Model 41
4.3.2 Underwater Image Formation Model 41
4.3.2.1 Distance between the Object and Camera 43
4.3.2.2 Remove the Artificial Light Source 43
4.3.2.3 Compensation the Light Scattering and Color Change 44
Chapter 5 Proposed Method of Underwater Image Enhancement 47
5.1 Introduction 47
5.2 Proposed Method 47
5.2.1 Dehazing 47
5.2.2 Color Transfer 48
5.3 Experimental Results 52
5.4 Conclusions 54
Chapter 6 Related Work of Rain/snow Removal in Single Image 55
6.1 Introduction 55
6.2 Guided Filter 56
6.2.1 Image Model of a Rain Streak of Snow Streak 57
6.2.2 Rain and Snow Removal 57
6.2.2.1 Reference Image 58
6.2.2.2 Removal of Rain and Snow Using the Reference Image 59
Chapter 7 Proposed Method of Rain/snow Removal in Single Image 61
7.1 Introduction 61
7.2 Proposed Rain Removal Method 62
7.2.1 RGB to HSV 63
7.2.2 Merge Saturation and Visibility 65
7.2.3 High-pass Filter Process 66
7.2.4 Directional Filter Process 68
7.2.5 Rain Pixels Compensation 71
7.3 Experimental Results 74
7.4 Proposed Snow Removal Method 77
7.4.1 RGB to HSV 78
7.4.2 Stretching Visibility and Saturation 79
7.4.3 Merge the Visibility and Saturation 81
7.4.4 Remove Snow Background from Image 81
7.4.4.1 Bilateral Filter 82
7.4.4.2 Separate Snow Background and Removal Candidate Region 83
7.4.5 Mark Snow in the Removal Candidate Region 84
7.4.6 Compensate for the Snow Points 85
7.5 Experimental Results 87
7.6 Conclusions 90
Chapter 8 Conclusions and Future Work 91
REFERENCE 93


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