FocusClear®處理過之生物顯微鏡影像隨深度變化的反疊積

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研究生：	王聖雰 Wang, Sheng-Fen
論文名稱：	FocusClear®處理過之生物顯微鏡影像隨深度變化的反疊積 Depth-variant Deconvolution of FocusClear® Processed Biological Microscopic Images
指導教授：	陳永昌 Chen, Yung-Chang
口試委員:	賴尚宏盧鴻興
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	59
中文關鍵詞：	反疊積、生物顯微鏡影像
外文關鍵詞：	deconvolution, Biological Microscopic Image
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現今，由於顯微鏡的成像技術越來越進步，二維的生物細胞組織取像品質也隨之提升許多。因此，更進一步的目標便是希望能夠將二維的成像推展至三維超解析度，讓整個生物細胞組織的架構可以完整的呈現在我們眼前，並且有利於接下來的分析。
本研究的目的是跨領域合作以期增加顯微鏡的三維顯微鏡影像解析度。更進一步希望研發出低價卻高解析度的顯微鏡。這個研究方法是利用一疊二維生物細胞組織影像還原成原本立體的生物細胞組織影像。每個二維生物細胞組織影像皆是用共軛焦顯微鏡取像得來的。藉著共軛焦顯微鏡的技術和後續的影像處理，我們期望不只能得到清晰的二維生物細胞組織影像，還能因此重建出原本的三維生物細胞組織影像。
本篇論文的研究方向著重在如何處理在不同深度上截取的影像，因為在生物組織不同深度上的點擴散方程式(Point Spread Function)是不一樣的，這是由於不同深度上的光線會受不同程度的球面像差問題影響所致。在本篇論文裡，我們修正了原本不受深度影響的點擴散方程式，讓它可以較符合在我們實驗裝置下截取影像的結果，並且用反疊積(Deconvolution)的方法來得到較清晰的影像。在這篇論文裡會介紹如何建立一個可以隨深度變化的點擴散方程式，並且將這個建立好的模型，代入可以隨深度變化用不同的點擴散方程式來做反疊積的方法(Depth-variant Deconvolution)，因此得到品質較好的影像。

Nowadays, 2D image quality of biological tissue has increased drastically because of the progress in image acquisition by microscope. Therefore, 3D superresolution from 2D images is the next aim. If achieved, it can make the structure of the entire biological tissue visualized clearly. Moreover, it is beneficial to the subsequent analysis.
This research is intended to increase the resolution of 3D microscopy images which is an interdisciplinary project to build up a cheap but high resolution microscope. The method of this work is to recover genuine 3D biological tissue images from stacks of 2D images. Each 2D biological tissue image is made from confocal fluorescence microscope. By this technique and the subsequent image processing, we intend not only to get clear 2D biological tissue images, but also to reconstruct the genuine 3D biological tissue images.
This research focuses on an algorithm for processing a stack of 2D images captured by the microscope from different depth of focuses. Since the point spread functions suffer from the spherical aberration in our microscope, they are no more space-invariant models. Therefore, the research is focused on how to modify theoretical point spread functions to adapt to our capturing image system and obtain deblurred images by deconvolution. In the research, the mathematical models of depth-variant point spread functions will be formulated. Furthermore, the depth-variant deconvolution algorithm that incorporates the depth-variant point spread functions will be introduced.

Table of Contents
Abstract    ii
Table of Contents    iv
List of Figures    viii
List of Tables    ix

Chapter 1 Introduction    1
1    Overview of 3D Super-Resolution    1
2    Motivation    2
3    Thesis Organization    3
Chapter 2 Previous Works    4
1    3D Super-Resolution  4
1.1    Confocal Fluorescent Microscope  4
1.2    Rotational Stage  6
1.3    Image Stacks with Different Angles  6
1.4    Deconvolution  7
1.5    Registration  8
1.6    Interpolation  9
2    3D Convolution Model  10
2.1    Image Formation    10
2.2    Convolution Model    12
3    3D Blind Deconvolution    13
3.1    3D Blind Deconvolution Flow Chart    13
3.2    Blind Deconvolution  14
4    Experimental Results and Discussions  15
4.1    Experimental Results  15
4.2    Discussions  18
Chapter 3 Depth-variant Point Spread Function  19
1    Theoretical PSF  19
1.1    Properties  19
1.2    Introduction  20
2    PSF of Confocal Fluorescence Microscope  21
2.1    Properties  22
2.2    Mathematical Model and Approximation  22
2.3    Problems 25
3    Depth-variant PSF of Confocal Fluorescence Microscope  25
3.1    Captured Images 26
3.2    Properties  27
3.3    Mathematical Model  29
3.4    Experimental Results  31
Chapter 4 Depth-variant Deconvolution    33
1    Blind Deconvolution  33
1.1    Flowchart  33
1.2    Initial Guess  35
1.3    Mathematical Model  35
1.4    Point Spread Function Constraints  38
2    Depth-variant Deconvolution  40
2.1    Flowchart  40
2.2    Depth-variant Imaging Model 41
2.3    Restoration Algorithm  44

Chapter 5 Experimental Results and Discussions 47
1    Simulation Experiment  47
1.1    One Object  47
1.2    Two Objects  49
2    Real Data Experiment  52
3    Discussions  54
Chapter 6 Conclusions and Future Works  55
1    Conclusions  55
2    Future works  56

                                

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