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研究生:林裕貿
研究生(外文):Yu-Mao Lin
論文名稱:用影像合成達成相機模組之影像穩定
論文名稱(外文):Image Stabilization System on a Camera Module with Image Composition
指導教授:傅楸善傅楸善引用關係
指導教授(外文):Chiou-Shann Fuh
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
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:2006
畢業學年度:94
語文別:英文
論文頁數:59
中文關鍵詞:防手震影像合成
外文關鍵詞:Image stabilizationSIFT
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隨著現今數位相機以及手機相機的畫素數量大幅提昇、以及相機體積大幅的縮小,越多越多的新科技是希望能提升相片的品質。在這些技術當中,防手震技術是為了減少或是避免因為手震所產生的模糊影像。現今主要有兩類的防手震方式,分別是光學式、以及數位式防手震。
在這篇論文裡面,我們提出從四張影像合成出一張沒有手震影像的系統。利用影像合成的技術以及從實驗上的比較,證明我們能夠重建出一張亮度正常且為銳利的影像。
With the boosting of number of image sensor’s pixels and the compacter working volume of today’s digital still camera or camera phone, the need for better image quality has soared and drives more newly designed image processing techniques. Image stabilization, one of these newly techniques, plays an essential role in today’s camera design. As its words suggest, image stabilization is a system designed to reduce the amount of image blur due to human’s handshake or even preventing the chances of a blur. Currently, image stabilization can be carried out by optical lens solution or by digital image processing technique.

In this thesis, we propose a digital image stabilization algorithm based on an image composition technique using four source images. By using image processing techniques, we are able to reduce the amount of image blur and compose a sharper image from four source images.
Chapter 1 Introduction ...................................................................................................1
1.1 Causes of blurred image...................................................................................2
1.2 Formation of blurred image .............................................................................3
1.3 Gradient Magnitudes........................................................................................5
1.3.1 Sobel Edge Detector .............................................................................6
1.3.2 Other Edge Detectors............................................................................7
Chapter 2 Optical Image Stabilization ...........................................................................8
2.1 Image Stabilization by Prisms .........................................................................8
2.2 Image Stabilization by Moving Lens.............................................................10
2.3 Image Stabilization by Moving CCD ............................................................12
Chapter 3 Digital Image Stabilization ..........................................................................13
3.1 Digital Image Stabilization by Moving Window...........................................13
3.2 Digital Image Stabilization by Higher ISO Speed.........................................14
3.3 Digital Image Stabilization on Camera Phone...............................................14
Chapter 4 Image Stabilization with Super Resolution Reconstruction ........................17
4.1 Concept ..........................................................................................................17
4.2 Feature Detection ...........................................................................................19
4.2.1 Harris Corner Detector........................................................................19
4.2.2 Analyzing Eigenvalues .......................................................................20
4.2.3 Corner Response .................................................................................21
4.3 Feature Matching ...........................................................................................24
4.4 Image Combination........................................................................................27
4.5 Experimental Results .....................................................................................28
4.6 Conclusion .....................................................................................................29
Chapter 5 Image Stabilization with Image Composition .............................................31
5.1 Feature Detection Using SIFT (Scale-Invariant Feature Transform) ............32
5.1.1 Scale-Space Extrema Detection..........................................................32
5.1.2 Keypoint Localization.........................................................................35
5.1.3 Orientation Assignment ......................................................................36
5.1.4 Keypoint Descriptor............................................................................37
5.1.5 SIFT Result .........................................................................................38
5.2 Feature Matching ...........................................................................................40
5.3 Pre-Rotation ...................................................................................................42
5.4 Binary Tree Image Composition....................................................................45
5.5 Flowchart of Our Algorithm ..........................................................................47
5.6 Experimental Results .....................................................................................48
Chapter 6 Conclusion and Future Work .......................................................................56
6.1 Dark Image Feature Detection .......................................................................56
6.2 Speed up Feature Matching ...........................................................................56
6.3 Image Composition........................................................................................56
[1]J. F. Chen, “Image Stabilization with Best Shot Selector and Super Resolution Reconstruction,” Master Thesis, Department of Computer Science and Information Engineering, National Taiwan University, 2005.
[2]Y. Y. Chuang, “Feature Matching,” http://www.csie.ntu.edu.tw/~cyy/courses/vfx/05spring/lectures/handouts/lec04_feature.ppt, 2005.
[3]CNET, “CNET Glossary: Image Stabilization (Optical, Electronic) - CNET Reviews,” http://reviews.cnet.com/4520-6029_7-6160688-1.html, 2006.
[4]Dpreview, “Minolta DiMAGE A1 Review 1. Introduction: Digital Photography Review,” http://www.dpreview.com/reviews/minoltadimagea1/, 2003.
[5]EDN, “Image Stabilization Shows Diversity of Engineering Approaches - 10-26-2000 – EDN,” http://www.edn.com/article/CA47280.html#ref, 2000.
[6]R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing using MATLAB, Prentice-Hall, Upper Saddle River, New Jersey, 2004.
[7]C. Harris, M. Stephens, A Combined Corner and Edge Detector, Proceedings of Alvey Vision Conference, Manchester, England, pp. 147-151, 1998.
[8]R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, Vol. I, Addison Wesley, Reading, MA, 1992.
[9]Howstuffworks, “Howstuffworks: How Gyroscopes Work,” http://www.howstuffworks.com/gyroscope.htm, 2006.
[10]Konica Minolta, “KONICA MINOLTA, Anti-Shake Technology,” http://konicaminolta.com.hk/ph/eng/products/photographic/dc/detail_7d.html
[11]D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
[12]D. G. Lowe, “Method and Apparatus for Identifying Scale Invariant Features in an Image and Use of Same for Locating an Object in an Image,” United States Patent# 6711293, 2004.
[13]NEC, “N902i-NEC ワイワイもばいる- カメラ,”http://www.n-keitai.com/n902i/cmr.html, 2005.
[14]Nikon Imaging, “Nikon Imaging | Vibration Reduction,” http://nikonimaging.com/global/products/digitalcamera/coolpix/cppf/eng/vr_index.htm, 2006.
[15]Panasonic, “Technology that LUMIX takes the shake out,” http://panasonic.co.jp/pavc/global/lumix/technology/index.html, 2005.
[16]Phoneyworld, “NTT Docomo''s FOMA N902i, with Image stabilizer,” http://www.phoneyworld.com/newspage.aspx?n=1567, 2005.
[17]Wikipedia, “Gradient,” http://en.wikipedia.org/wiki/Gradient, 2006.
[18]Wikipedia, “Scale-Invariant Feature Transform,” http://en.wikipedia.org/wiki/Scale-invariant_feature_transform, 2006.
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