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研究生：	陳姿秀 Chen, Zih-Siou
論文名稱：	基於邊緣權重資訊的感知式銳利度強化演算法 A Perceptual Sharpness Enhancement Algorithm Based on Edge-Weighted Information
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：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	46
中文關鍵詞：	邊緣資訊、對比強化、梯度強化、直方圖強化
外文關鍵詞：	edge information, contrast enhancement, gradient enhancement, histogram enhancement
相關次數：	點閱：106 下載：2
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這篇論文的目的是提升影像的銳度，使其看起來像數位像機的照片，或者更好。有些濾波器會過分強化處理，使得圖像看起來不自然。在這篇論文中，我們提出了一個基於感知式的演算法來明顯的表現細節紋理，銳化整體影像。然後利用原始影像的邊緣資訊將其合併。
本演算法分兩個部分:梯度強化與直方圖強化。首先，在梯度強化部分，區域的銳度強化濾波器試著結合人眼視覺系統的概念，來得到更好的感知結果。這個銳度強化濾波器最重要的特點就是能完善的表現細節紋理並抵抗雜訊。再來，在直方圖強化部分，藉由加權式直方圖等化機制來廣域的調整整體直方圖，這個機制最大的能力就是藉由提升整體影像的對比來達到銳化邊緣的目的。我們提出一個基於邊緣資訊的方法來結合這兩個部分。實驗結果顯示，經過我們的演算法後詳細的細節紋理可以容易地被觀察到，整體影像看起來不僅更銳利且自然。

The goal of proposed algorithm is to enhance the images like the digital camera photos or, better. Some enhancement filters process too excessive that makes image look unnatural. In this thesis, we propose an algorithm to represent obviously the detailed textures and sharpen the overall image based on perceptual approach. Then the sharpened images are merged by the edge information of original image.
The proposed algorithm consists of two stages: gradient and histogram enhancement. First, in the gradient stage, the local sharpness filter is tried integrating the concept of Human Visual System (HVS) for better perceptual results. The strongest resist of noise and the well presentation of the detailed textures are the most significant features of this filter. Second, in the histogram stage, the global histogram is adjusted by a histogram–based weighting mechanism. The great ability of this mechanism is sharpening the edge by enhancing the contrast of whole image. Therefore, we propose an approach based on edge information to combine these two stages.
The experimental results show that the detailed textures can be easily observed .The whole image looks not only more sharp but also natural after applying our algorithm.

Contents


Contents                                                                i
List of Tables                                                           iii
List of Figures                                                           iv
Chapter 1   Introduction                                                 1
Chapter 2    Background	                                             2
1 Human Visual System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1 Light Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Brightness Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Weber-Fechner Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
2 Gradient Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Sharpening Spatial Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Sobel Filter . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Weighted Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
2.4 Quadratic Weighted Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10
3 Histogram Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
3.2 Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Weighted Thresholded Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 3	The Proposed Algorithm                                     14
1 Gradient Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.1 The Analysis of Quadratic Weighted Median Filter . . . . . . . . . . . . . . . . . . . . . 16
1.2 The Proposed Method: Clipped Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Histogram Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1 The Analysis of Weighted Thresholded Histogram Equalization . . . . . . . . . . . 22
3 The Combination of Gradient and Histogram Enhancement . . . . . . . . . . . . . . 24
3.1 The Opinions of Pixels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 The Proposed Method: Edge-Weighted Contrast Enhancement . . . . . . . . . . . . . .24
Chapter 4	Simulate Results                                            27
1 Comparison with Other Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 Overall Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Chapter 5	Conclusion and Future Work                                 40
1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
REFERENCE                                                          42












List of Tables


1	The Average Luminance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2	The Standard Deviation of Luminance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32


















List of Figures


1 The distribution of rods and cones across the retina . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Threshold versus intensity (TVI) functions of rods and cones . . . . . . . . . . . . . . . . . . . 4
3 Block diagram of unsharp masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 3×3 Laplacian Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5 Sobel Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
6 WTHE control curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1 Block diagram of the proposed enhancement method . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Structure of QWMF unsharp masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Positive and negative-slope edge enhancement with QWMF . . . . . . . . . . . . . . . . . . . 18
4 Example of overshoot and undershoot effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Flowchart of clipped median filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6 Overshoot and undershoot detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7 Compared histogram of WTHE and brightness shifted result . . . . . . . . . . . . . . . . . . . 23
8 Flowchart of edge-weighted contrast enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1 Different sharpness enhancement result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2 Different sharpness enhancement result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3 Different sharpness enhancement result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4 A part of magnified Fig.4.1 result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Compared with [37] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6 Compared with [37] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
7 Simulation results of different building images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
8 Simulation results of different landscape images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
9 Simulation results of different object images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

                                    

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校內：2015-08-12公開
校外：2015-08-12公開

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