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研究生:楊東祐
研究生(外文):Tung-yu Yang
論文名稱:視訊與單相機立體成像之影像處理
論文名稱(外文):Image Processing for Interlaced Video and Planar Catadioptric Stereo
指導教授:吳先晃
指導教授(外文):Hsien-huang Wu
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系碩士班
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:2004
畢業學年度:92
語文別:中文
論文頁數:93
中文關鍵詞:影像放大解交錯
外文關鍵詞:de-interlacingadapterimage enlargement
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近幾年來電腦視覺與視訊系統的技術快速發展,而交錯掃描(interlaced scan)一直是過去幾十年來視訊傳輸與儲存的主要格式,然而近幾年來,顯示設備與視訊技術都有長足的進步,使得高解析度的循序掃描(progressive scan)格式逐漸廣泛的被採用。交錯掃描格式的視訊信號需要在循序掃描顯示裝置上顯示,或是提升交錯掃描格式的視訊訊號解析度時,則解交錯演算法能提供交錯至高畫質循序掃描格式的轉換。且高畫質循序顯示器的視訊影像解析度的需求相當大,低解析度的影像在高畫質的畫面播放時,視訊品質會產生劣化,因此,解交錯演算法與提高影像的解析度將是重要的一環。在此篇論文中,我們針對解交錯演算法與提高影像解析度各自提出一套新的方法,兩者都是透過偵測多角度的邊緣方向,且依據沿著邊緣的像素差異小及垂直邊緣的像素差異大的觀念開發新方法。現代的3D影像應用的熱潮將越形擴大,在立體視覺 (3D vision) 的應用上,3D視訊影像的擷取則利用攝影機加掛Adapter光學鏡頭,可降低立體視覺系統的架設成本,但取得的立體影像垂直資訊是減半的;對於一些觀看立體視訊的雙投影機,或是針對兩個眼睛各自觀看一台螢幕所產生的立體效果,這些設備對於立體影像的需求是左右影像為完整的圖像。因此,運用類似於上述的解交錯方法,提高立體影像的品質,使觀看時有更佳的立體臨場感。將各領域現有的方法與本文中所提出的演算法做比較,雖PSNR沒有太大的差別,但從人眼觀看的角度可發現後者有更佳的影像品質且使3D視訊影像更具有立體感。
The technology of computer vision and video system has developed rapidly in recent years. Interlaced scan is the major form of video transmission and storage for the past years. However, display facilities and video techniques have made great progress such that progressive scan with high resolution has being widely adopted gradually. When the video signal of interlaced scan needs to displayed on progressive scan display devices, de-interlacing algorithm can provide the transformation to progressive format. Since there is a great requirement on resolution of video sequence in the high definition progressive monitor. When low resolution image is showed on high definition screen will be deteriorated. Therefore, de-interlacing and increase of the image resolution of images are important issues. In this thesis, we propose a new method of de-interlacing and then increase the resolution of images. Both of them are based on the idea that pixels has small difference along edge and large difference cross edge. The new method precisely detect edge directions by checking a variety of angles.
The popularity of 3D imaging application will advance in the future, and using the camera with adapter to obtain 3D video sequence can reduce the cost of setting up stereo vision system. However, much research has been devoted to investigating the generation of stereoscopic video based on catadioptric stereo systems. These systems, which utilize proper combination of mirrors and lens, can capture stereoscopic video using only one camera. The left and right images are multiplexed to generate an interlaced video with each image corresponds to one field. This field-sequential format unavoidably reduces vertical resolution of the image pair by half. In this thesis, we proposed a notion called stereoscopic de-multiplexing to recover the original resolution of the image pairs from the field-sequential video. In order to increase the sharpness of the image and avoid artifacts, interpolation along the edge direction is emphasized. Several de-interlacing methods were evaluated, and a new edge adaptive approach was proposed. This new method can estimate edge direction with higher accuracy, and image quality of experimental results show that the proposed algorithm outperforms the other popular de-interlacing methods.
中文摘要 i
英文摘要 ii
誌 謝 iv
目 錄 v
表 目 錄 viii
圖 目 錄 ix

第一章 緒論 1
1.1 研究動機 1
1.2 研究目標 1
1.3 研究大綱 2
第二章 解交錯技術之相關研究 4
2.1 原理簡介 4
2.2 空間的解交錯技術 6
2.2.1 Line Repetition 6
2.2.2 Line Average 7
2.2.3 Edge-based Line Average 7
2.3空間-時間的解交錯技術 9
2.3.1 Field insertion 9
2.3.2 Vertical-Temporal filter 10
2.3.3 Vertical-Temporal Median Filter 11
2.3.4 Weighted median Filter 11
2.4 移動適應性演算法之技術 13
2.5 移動補償技術 13
2.5.1 Motion Compensated Median Filtering 14
2.5.2 Motion Compensated Time-Recursive De-interlacing 15
參考文獻 17
第三章 影像放大技術之相關研究 18
3.1傳統的內插法 18
3.1.1最鄰近內插法 18
3.1.2 雙線性內插法 19
3.1.3 雙立方內插法 21
3.2 邊線保留及有方向性的內插 22
3.3 超解析演算法 22
3.3.1 原理 22
3.3.2 相關研究 26
參考文獻 27
第四章 以方向性插補做解交錯處理 29
4.1 簡介 29
4.2 Edge-based Line Averaging之介紹與缺點 29
4.3 拓展邊緣偵測與平均插補交錯之方法 31
4.3.1 移動偵測 32
4.3.2 拓展邊緣偵測與插補 33
4.3.3垂直偵測之平均 38
4.4 實驗結果 39
4.5 結論 42
參考文獻 43
第五章 基於適應性與邊緣察覺插補法之放大影像之解析度 44
5.1 簡介 44
5.2 提出的方法 46
5.2.1 Adaptive Interpolation 46
5.2.1.1 Interpolate of Homogenous area 49
5.2.1.2 Interpolate of Edge Pixels 50
5.2.2 Edge Awareness Interpolation 56
5.3 實驗結果 59
5.3.1 Sharpness值之客觀評估方式 59
5.3.2 PSNR值之客觀評估方式 60
5.3.3 人眼觀看之主觀評估方式 61
5.4 結論 64
參考文獻 65
第六章 以邊緣方向適應性插補於立體影像 67
6.1 簡介 67
6.2 adapter之特性 68
6.3 研究方法 71
6.3.1 Color Modification 72
6.3.2 Detect Homogenous area and Interpolation 73
6.3.3 Detect-edge of Neighbor 3x3 area and Interpolation 74
6.3.4 Extended Edge Detect and Interpolation 75
6.3.5 Median and Average Interpolation 81
6.4 實驗結果 82
6.4.1 四個彩色影像之客觀與主觀評估方式 82
6.4.1.1 PSNR值之客觀評估方式 82
6.4.1.2 人眼觀看之主觀評估方式 83
6.4.2 立體影像對之評估方式 87
6.5 結論 90
參考文獻 91
第七章 總結 92
作者簡介 93
ch.2
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ch.3
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[17] P. H. Chang, J. J. Leou, and H. C. Hsieh, “A Genetic Algorithm Approach to Image Sequence Interpolation,” Signal Processing: Image Commu., pp. 506-520, 2001

ch.4
[1] C.J. Kuo, C. Liao, and C.C. Lin, “Adaptive interpolation technique for scanning rate conversion,” IEEE Trans. Circuits Systems for video Technology, vol. 6, no. 3, pp. 317-321, Jun. 1996.
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[3] G.D. Haan and E.B. Bellers, “De-interlacing – An Overview,” in Proc. IEEE, vol. 86, no. 9, pp. 1839-1857, Sep.1998.
[4] R. Li, B. Zeng, and M.L. Liou, “Reliable motion detection/compensation for interlaced sequences and its applications to deinterlacing,” IEEE Trans. Circuits Systems for Video Technology, vol. 10, no. 1, pp. 23-29, Feb. 2000.
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ch.5
[1] T. S. Huang and R. Y. Tsai, “Multi-frame image restoration and registration,” Advances in Computer Vision and Image Processing, Vol. 1, pp. 317-339, 1984.
[2] H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Computer Vision, Graphics, and Image Processing, Vol. 54, No. 2, pp. 181-186, March 1992.
[3] M. C. Chiang and T. E. Boult, “Local blur estimation and super-resolution,” In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, pp. 821-826, San Juan, Puerto Rico, June 1997.
[4] S. Kim, N. Bose, and H. Valenzuela, “Recursive reconstruction of high resolution image from noisy undersampled multiframes,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 38, pp. 1013-1027, June 1990.
[5] M. Irani and S. Peleg, “Motion analysis for image enhancement: Resolution, occulsion, and transparency,” Journal of Visual Communication and Image Representation, Vol. 4, No. 4, pp. 324-335, Dec. 1993.
[6] R. Schultz and R. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Transactions on Image Processing, Vol. 5, No. 6, pp. 996-1011, June 1996.
[7] R. C. Hardie, K.J. Barnard, and E. E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Transactions on Image Processing, Vol. 6, No. 12, pp. 1621-1633, Dec. 1997.
[8] M. Lehmann, Claudia Gonner, Klaus Spitzer, “Addendum: B-spline Interpolation in Medical Image Processing,” IEEE Trans. on Medical Image, vol. 20, pp. 660-665, July 2001.
[9] M. Unser, A. Aldroubi, M. Eden, “B-Spline Signal Processing: PartII-Efficient Design and Applications,” IEEE Trans. Signal Processing, vol. 41, pp. 834-848, Feb. 1993.
[10] M. Unser, “Spline: A Perfect Fit for Signal and Image Processing,” IEEE Signal Processing Magazine, pp. 22-38, Nov. 1999.
[11] H. S. Hou, H. C. Andrews, “Cubic Splines for Image Interpolation and Digital Filtering,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. ASSP-26, no.6, pp. 508-517, Dec. 1978.
[12] N. Plaziac, “Image interpolation using Neural Networks,” IEEE Trans. On Image Processing, vol. 8, on. 11, Nov. 1999.
[13] P.H. Chang, J.J. Leou, H.C. Hsieh, “A Genetic Algorithm Approach to Image Sequence Interpolation,” Signal processing: Image Commu., pp. 506-520, 2001.
[14] X. Li, M.T. Orchard, “New Edge-Directed Interpolation”, IEEE Trans. on Image Processing, vol. 10, pp. 1521-1527, Oct. 2001.
[15] R. Leizza, L. B. Dibio, M.G. Luiz, “A Locally Adaptive Edge-preserving Algorithm for Image Interpolation,” Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing, pp. 300-305, Oct. 2002
[16] C.H. Huang, M.J. Chen, C.T. Hsu, “Fast Edge-Oriented Image Interpolation Algorithm,” 2003 Workshop on Consumer Electronics, Nov. 2003
[17] H. Shi, R. Ward, “Canny edge based image expansion,” IEEE International Symposium on Circuits and Systems, vol. 1, pp. I-785 -I-788, May 2002
[18] R. R. Schultz and R. L. Stevenson, “A Bayesian Approach to Image Expansion for Improved definition,” IEEE Trans. Image Processing, Vol. 3, pp. 233-242, May 1994
[19] R. R. Schultz and R. L. Stevenson, “Extraction of High-resolution Frame from Video Sequences,” IEEE Trans. Image Processing, Vol. 5, pp. 996-1011, June 1996
[20] P. H. Chang, J. J. Leou, and H. C. Hsieh, “A Genetic Algorithm Approach to Image Sequence Interpolation,” Signal Processing: Image Commu., pp. 506-520, 2001.
[21] Y. C. Lan, “Adaptive digital zoom techniques based on hypothesized boundary,” master dissertation, National Taiwan Univ. 1999.

ch.6
[1] Y. Kim, “Deinterlacing algorithm based on sparse wide vector correlations,” SPIE Optical Engineering, vol. 2727, pp. 89-99, 1996.
[2] H. S. Oh, Y.Y. Jung, A. W. Morales, and S. J. Ko, “Spatio-temporal edge-based median filtering for deinterlacing,” in Digest of the Int. Conference on Consumer Electronics, pp. 52-53, June 2000.
[3] H. Yoo, and J. Jeong, “Direction-oriented interpolation and its application to de-interlacing,” IEEE Trans. Consumer Electronics, vol. 48, no. 4, pp. 954-962, Nov. 2003.
[4] W. Woo, N. Kim, Y. Iwadate, “Stereo Imageing Using a Camera with Stereoscopic Adapter,” IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1512 – 1517, Oct. 2000.
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