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研究生:范涵茹
研究生(外文):Han-RuFan
論文名稱:一個基於分區曝光系統對比強化適應演算法
論文名稱(外文):An Adaptive Contrast Enhancement Algorithm Based on Zone System
指導教授:戴顯權戴顯權引用關係
指導教授(外文):Shen-Chuan Tai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:2012
畢業學年度:100
語文別:英文
論文頁數:83
中文關鍵詞:分區曝光系統亮度強化對比強化
外文關鍵詞:Zone SystemLuminance enhancementContrast enhancement
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  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:1
當光源過亮或是過暗時,在暗部或亮部的細節可能無法在顯示器上完整呈現;且有些自然場景的動態範圍超過人眼所能感受的範圍。因此,攝影設備及人眼知覺之間的差異將成為完整呈現影像場景的瓶頸。影像強化是一個可以改善影像品質及人眼視覺感受的方法。此外,影像強化可應用在許多領域,例如:遙測、醫學影像分析、圖形辨識、高解析度電視、顯微造影技術等。
此篇論文提出一個基於分區曝光系統對比強化適應演算法,用來改善影像的視覺品質,及呈現隱藏的細節及增加影像對比,尤其是當拍攝場景極缺乏亮度或是亮度不均時,例如:夜間攝影、背光場景等。本演算法由兩個步驟所構成,適應性全域亮度強化法及適應性區域對比強化法。適應性全域亮度強化法是一個基於分區曝光顯影系統資訊的全域亮度轉換函數,此步驟不但在過暗區域提升亮度且在過亮區域降低亮度。適應性區域對比強化法藉由區域亮度的不連續性調整相鄰像素之間的亮度,進而改善區域的對比度,且清楚呈現影像細節。
實驗結果顯示,我們所提出的演算法在加強對比度、保留細節特性及銳化物件邊緣方面都有不錯的效果。且經我們的演算法處理後的影像,其細節紋理可容易被觀察到。並基於主觀及客觀的評估方法,可證明我們提出的演算法在影像強化方面十分有效。且經由這些效能評估方法,可知道我們的方法相較於其他方法,例如:結合相鄰像素的非線性強化演算法(AINDANE)、針對彩色影像的基於區域方法之非線性轉換函數強化演算法(NTFBLA)、結合銳度補償的可調式銳利度強化演算法(ALTHE)及基於權重並結合分群之直方圖等化的對比強化演算法(WBCHE),我們所提出的方法可產生較佳的影像。

If the lighting condition is over bright or over dark, the details in the brightest region or shadow region are usually hidden on the display device. And some of the natural images that exceed dynamic range of human eyes perceive. Thus, a serious difference between capturing device and human perception lead to the bottleneck in representing an image completely. The image enhancement is a method to improve the image quality to approach human perception. Besides, image enhancement could apply to many fields, such as remote sensing, medical image analysis, pattern recognition, high definition television, microscopic imaging, and so on.
This thesis presents an adaptive contrast enhancement algorithm based on Zone System to improve the visual quality of digital images that are captured under extremely low or non-uniform lighting conditions, for example photographing at night, or carrying the situation facing backward the light. And the proposed algorithm could reveal hidden image details or increase the contrast of an image with low dynamic range. The proposed algorithm is comprised two processes: adaptive global luminance enhancement and adaptive neighborhood-dependent contrast enhancement. The adaptive global luminance enhancement algorithm is a global intensity transform function based on Zone System information. This process not only upgrades the luminance of darker regions but also degrades the luminance of brighter regions. The adaptive neighborhood-dependent contrast enhancement adjusts the intensity of each pixel based on the discontinuities of the local luminance. It also improves the contrast of local region and reveals the details of image clearly.
Experimental results show that the proposed algorithm has good performance on enhancing contrast, preserving more detail of characteristics and sharpening edges of objects. We could observe textures and details easily after applying the proposed algorithm. Based on subjective and objective evaluations, the proposed algorithm proves the efficient performance in image enhancement. Those criteria of evaluations show that the proposed algorithm produces better enhanced images in comparison with other algorithms like AINDANE, NTFBLA, ALTHE and WBCHE.

Contents i
List of Figures iii
List of Tables x
Chapter 1 Introduction 1
1.1 Motivation and Recent Work 1
1.2 Organization of This Thesis 4
Chapter 2 Background 6
2.1 Zone System 6
2.2 Human Visual System 11
2.2.1 Light Receptors 12
2.2.2 Brightness Adaptation 15
2.2.3 Weber-Fechner Law 16
2.3 Histogram 17
2.4 Related Works 17
2.4.1 Adaptive and Integrated Neighborhood Dependent Approach for Nonlinear
Enhancement (AINDANE) 20
2.4.2 Nonlinear Transfer Function-Based Local Approach (NTFBLA) 22
2.4.3 Average Luminance Threshold Histogram Equalization (ALTHE) 23
2.4.4 Weight-Based Cluster Histogram Equalization (WBCHE) 24
Chapter 3 The Proposed Algorithm 26
3.1 Framework of the Proposed Algorithm 26
3.2 The Color Space 27
3.3 Adaptive global luminance enhancement 30
3.4 Adaptive Neighborhood-Dependent Contrast Enhancement 40
3.4.1 Spatial Filters 40
3.4.2 Adaptive neighborhood-dependent contrast enhancement 42
Chapter 4 Experimental Results and Comparisons 46
4.1 Experimental Results 47
4.2 Comparisons with other methods using the contour map 56
4.3 Comparisons with other methods using the histogram 64
4.4 Comparisons with other methods using the statistical method 73
Chapter 5 Conclusion and Future Work 77
Bibliography 78
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