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研究生:游正義
研究生(外文):Cheng-Yi Yu
論文名稱:適應性反雙曲線正切函數對影像對比增強之研究
論文名稱(外文):Image Contrast Enhancement Based on Adaptive Inverse Hyperbolic Tangent
指導教授:歐陽彥杰
指導教授(外文):Yen-Chieh Ouyang
口試委員:張建禕馬代駿楊晴雯林正堅陶金旭廖俊睿
口試日期:2011-06-27
學位類別:博士
校院名稱:國立中興大學
系所名稱:電機工程學系所
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:2011
畢業學年度:99
語文別:英文
論文頁數:127
中文關鍵詞:基於多區段參數調整的適應性反雙曲線正切函數基於多區段參數調整的適應性反雙曲線正切函數基於多區段參數調整的適應性反雙曲線正切函數基於多區段參數調整的適應性反雙曲線正切函數基於多區段參數調整的適應性反雙曲線正切函數基於多區段參數調整的適應性反雙曲線正切函數
外文關鍵詞:human visual perceptionimage contrast enhancementAdaptive Inverse Hyperbolic Tangent (AIHT)Multi-Segment parameter adjustment of Adaptive Inverse Hyperbolic Tangent (MSAIHT)Contrast-limited adaptive histogram equalization (CLAHE) modulation based on MSAIHT contrast enhancement (MSAIHT⊕CLAHE)
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在人的視覺感知中對比度對影像的品質有很大影響。不佳的拍攝環境將影響所拍攝影像的對比度,並產生一張意想不到的影像。本研究提出一些快速且有效的影像對比增強演算法來改善在顯示場景時的對比品質,包括適應性反雙曲線正切函數(AIHT)演算法、多區段參數調整的適應性反雙曲線正切函數(MSAIHT)演算法、基於多區段參數調整的適應性反雙曲線正切函數之對比限制的適應性區域直方圖等化調變影像對比增強演算法(CLAHE⊕MSAIHT)。
適應性反雙曲線正切函數演算法是用來改善顯示一個場景的品質與對比度。因為數位相機的要求是維持焦距裡主要目標物的明亮度,例如人臉區域的亮度分佈。根據此需求,大多數的數位相機皆採用伽瑪函數(gamma function)來做為影像增強的基礎。然而,使用此種影像增強法常會造成主要目標物與背景的對比變差。為了解決這個問題,對比增強演算法被廣泛的應用於調整影像的對比,並架構於人類視覺直觀的感知上。原始影像的對比類型是使用新的判斷準則來決定。反雙曲線正切函數演算法的轉換參數是根據不同的對比類型來做適應性的調整,因此參數的調整空間就相當廣。本方法不僅維持原有的直方圖分布形狀特徵且能有效地提升影像的對比品質。
多區段參數調整是用來改善適應性反雙曲線正切函數演算法。在了解與觀察場景之細節和邊緣時它具有衆所周知的人類視覺系統(HVS)感知能力。我們的主要目標是開發一個對比增強技術來復原模糊不清且黑暗的影像,並且加強它的視覺品質。多刻度係數是編修適應性反雙曲線正切函數演算法的參數。所提出的MSAIHT方法是在AIHT演算法執行之前,先由區域平均值進行分段,然後以分段後的子段分別進行AIHT對比增強。我們顯示這個方法能提供一個方便和有效的機制去控制增強處理,且更能適應各種類型的影像。實驗結果顯示AIHT演算法能適應性地增強原始影像全域性的對比度,並同時將目標物細節突顯出來。而MSAIHT能够適應性地增強原始影像的區域性對比度,且比AIHT更能將目標物細節突顯出來。
雖然AIHT和MSAIHT演算法皆能適應性地增強原始影像的對比度,但是並沒有達到最好的對比增強效能。所以我們也提出結合MSAIHT與CLAHE之多區段參數調整的適應性反雙曲線正切函數之對比限制的適應性區域直方圖等化調變影像對比增強演算法。CLAHE演算法有好的對比增強的效能,但是過度的對比增強將導致嚴重的色差結果。我們應用MSAIHT與CLAHE演算法的優點提出多重處理的影像對比增強演算法,以達到更好的對比增強效果。

Contrast has a great influence on the quality of an image in human visual perception. A poorly-illuminated environment can significantly affect the contrast ratio, producing an unexpected image. As a sequence we have developed a fast and effective mechanism for image contrast enhancement. This mechanism includes the use of adaptive inverse hyperbolic tangent (AIHT) algorithm, Multi-Segment parameter adjustment of adaptive inverse hyperbolic tangent (MSAIHT) algorithm, and contrast-limited adaptive histogram equalization (CLAHE) modulation based on MSAIHT contrast enhancement (CLAHE⊕MSAIHT) algorithm.
The AIHT algorithm can be used to improve the display quality and contrast of a scene. In general, digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face and a gamma function is generally used for this purpose. However, the use of gamma function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT algorithm can determine the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively.
The Multi-Segment parameter adjustment is a nature extending of Adaptive Inverse Hyperbolic Tangent (MSAIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. Our main goal is to produce a contrast enhancement technique to recover an image from a blurred and darkness, also improve visual quality at the same time. Multi-scale coefficients adjustments can provide a further local refinement in detail under the Adaptive Inverse Hyperbolic Tangent algorithm. The proposed MSAIHT method is using the sub-band to calculate the local mean and local variance before the AIHT algorithm is performed. We also show that this approach is convenient and effective to do the enhancement process for a various types of images. Experimental results show that the AIHT algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously. The MSAIHT is also capable of enhancing the local contrast of the original image adaptively while extruding more on the details of objects simultaneously.
We also propose a CLAHE modulation based on MSAIHT contrast enhancement algorithm from conjugate MSAIHT and CLAHE image contrast enhance (MSAIHT⊕CLAHE) algorithm. The CLAHE has good contrast enhance performance, but excessive contrast enhance will produce the serious chromatic aberration results. We apply the MSAIHT and CLAHE advantage to present a joint multiple processes algorithm of contrast enhancement to achieve better contrast enhancement effect.

List of Figures vii
List of Tables xii
Chapter 1 Introduction 1
Chapter 2 Contrast Enhancement for an Image 8
2.1. Linear Contrast Enhancement 9
2.1.1. Minimum-Maximum Linear Contrast Stretch 9
2.1.2. Percentage Linear Contrast Stretch 11
2.1.3. Piecewise Linear Contrast Stretch 11
2.2. Nonlinear Contrast Enhancement 13
2.2.1. Histogram Equalization 13
2.2.2. Logarithm Curve 18
Chapter 3 Adaptive Inverse Hyperbolic Tangent Algorithm 21
3.1. Inverse Hyperbolic Tangent Function 23
3.2. Symmetrical Bias Power Function 26
3.3. Enhancement Gain Function 29
Chapter 4 Multi-Segment Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm 32
4.1. Two-Segment Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm 33
4.2. Three-Segment Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm 36
4.3. Four-Segment Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm 39
4.4. Multi-Segment Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm 42
Chapter 5 CLAHE Modulation Based on MSAIHT Image Contrast Enhancement 46
5.1. Contrast-Limited Adaptive Histogram Equalization 48
5.2. CLAHE Modulation Based on MSAIHT Contrast Enhancement Algorithm 53
Chapter 6 Implementation and Experimental Results 58
6.1. Experimental Results of AIHT 58
6.2. Experimental Results of MSAIHT 69
6.3. Experimental Results of MSAIHT⊕CLAHE 79
Chapter 7 Conclusions 114
References 117
Biography 123

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