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研究生: 王乃慶
Wang, Nai-Ching
論文名稱: 一個以強度對分佈為基礎的自動化影像增強法
A Method for Automatic Image Contrast Enhancement Based on Intensity-Pair Distribution
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: 2008
畢業學年度: 96
語文別: 英文
論文頁數: 55
中文關鍵詞: 強度對對比增強
外文關鍵詞: contrast enhancement, intensity-pair distribution
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  • 強度對分佈(intensity-pair distribution)是近年來針對影像對比增強的新研究方法。它除了能有效地增強對比之外,亦能使得對比增強後雜訊不至於被放大。在此演算法中留有相當多可提供使用者自行調整的參數。而在適當的參數設定下,強度對分佈演算法確實有著令人激賞的表現。但是目前此種演算法中,並無有效選擇參數的方法。本文提出了一有效的準則來選擇參數,使得此演算法的效果能夠有效地發揮,並且透過疊代的方式,使得參數選擇具有自動化的能力。

    Intensity-pair distribution was recently proposed to enhance the contrast of an image. There are several parameters provided in that algorithm for users to control the enhancement. With a proper combination of parameters, the algorithm provides satisfying contrast enhancement without noise amplification, unnatural look, and other common drawbacks that are seen in previous works, but there is no effective method of parameter selection mentioned. In
    this paper, we proposed an effective criterion based on human visual system to decide a proper combination of parameters. With an appropriate combination of parameters, we can take the most advantage of that algorithm. Automation of parameter selection can easily be done by iteratively using the proposed algorithm. We presented experimental results to compare results of contrast enhancement with proper and improper combinations of
    parameters.

    Contents Acknowledgements iv Contents i List of Figures iii List of Tables v 1 Introduction 1 2 Background 4 2.1 Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Histogram Equalization . . . . . . . . . . . . . . . . . . . . . 7 2.3 Local Contrast Enhancement . . . . . . . . . . . . . . . . . . 8 2.4 Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.5 Human Visual System . . . . . . . . . . . . . . . . . . . . . . 10 2.5.1 Light Sensitivity . . . . . . . . . . . . . . . . . . . . . 10 2.5.2 Light Adaptation and Dark Adaptation . . . . . . . . 11 2.5.3 Contrast Masking . . . . . . . . . . . . . . . . . . . . . 12 3 Proposed Algorithm 13 3.1 Review of Intensity-Pair Distribution . . . . . . . . . . . . . . 14 3.2 Parameter Selection . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.2 Strength of Contrast Enhancement(SCE) . . . . . . . . 18 3.2.3 Criteria of Parameter Selection . . . . . . . . . . . . . 19 3.3 Automation of Algorithm . . . . . . . . . . . . . . . . . . . . 20 3.4 Time Complexity of Automatic Algorithm . . . . . . . . . . . 21 4 Experimental Results 23 4.1 Gray Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Color Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5 Conclusions and Future Work 50 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 References 52

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