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研究生： 潘映霖
Ying-lin Pan
論文名稱： 植基於梯度下可調式具有強邊效果的誤差擴散法
A Generalized Gradient-based Adaptive Error Diffusion Method with Edge Enhancement
指導教授： 楊維寧
Wei-ning Yang
鍾國亮
Kuo-liang Chung
口試委員: 陳雲軸
Yun-shiow Chen
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 32
中文關鍵詞： 邊相關性 、邊緣強化 、誤差擴散法 、梯度資訊 、Floyd-Steinberg model 、半色調 、PSNR
外文關鍵詞： Edge correlation, edge enhancement, error diffusion, gradient information, Floyd–Steinberg model, halftoning, PSNR.
相關次數： 點閱：241  下載：2 
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    • 在本篇論文中，我們提出一個植基於梯度下可調式具有強邊效果的誤差擴散法。在我們的方法中，不僅可以調整半色調中的門檻值並可動態調整誤差擴散的比重而將量化誤差擴散給鄰近的像素。在八張典型的測試圖中，實驗結果說明我們所提出的植基於梯度下可調式具有強邊效果的誤差擴散法與Floyd—Steinberg誤差擴散法、Eschbach and Knox所提出的方法、Hwang所提出的方法、Feng所提出的方法以及Li所提出的方法比較，有很好得折衷優點在強邊效果及影像品質上，但需增加一些時間的消耗。

    In this thesis, we propose a generalized gradient–based adaptive error diffusion method with edge enhancement effect. Our proposed method not only modulates the threshold in halftoning but also the weights of the error diffusion filter can be determined adaptively to diffuse the quantization error to neighboring pixels properly. Under eight testing images, experimental results demonstrate that our proposed generalized gradient–based adaptive error diffusion method has good compromised advantage of the edge enhancement effect in the halftone image and the image quality effect in the corresponding inverse halftone image when compared to Floyd–Steinberg error diffusion method, the method by Eschbach and Knox, the method by Hwang et al., the method by Feng et al., and the method by Li, but it has some execution–time degradation.

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 The Survey of Floyd–Steinberg Error Diffusion Method . . . . . . . . . . 4 3 The Proposed Generalized Gradient-based Adaptive Error Diffusion (GAED) Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1 Modulated threshold determination for the current pixel . . . . . . . . . 7 3.2 Determination of adaptive error diffusion filter . . . . . . . . . . . . 11 3.3 The proposed GAED algorithm . . . . . . . . . . . . . . . . . . . . . . 15 4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 18 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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    [17] Available: http://www.csee.wvu.edu/ xinl/code/halftone.zip.

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