政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/63212

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/63212

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题名:	影像縫補技術應用於以樣本為基礎的超解析度演算法之研究 Image Quilting for Example-based Super Resolution
作者:	郭勝夫 Kuo, Sheng Fu
贡献者:	廖文宏 Liao, Wen Hung 郭勝夫 Kuo, Sheng Fu
关键词:	超解析度補丁影像縫補紋理合成 super resolution patch image quilting texture synthesis
日期:	2013
上传时间:	2014-01-02 14:06:39 (UTC+8)
摘要:	高解析度影像含有較多的像素資訊，所以可以呈現出比低解析度影像更多的細節內容與色調變化，提升影像解析度的技術一直是數位影像處理的重要研究課題。在本論文中，我們實作了以樣本為基礎的超解析度演算法(example-based super resolution)，其主要是利用高解析度與低解析度影像在空間上相對應的高頻資訊作為樣本，用以估算出相對合理(plausible)的高解析度影像。在演算法中有兩個關鍵的因素會影響執行結果的品質，一個是補丁合成的方法，另一個則是訓練資料的選擇。我們嘗試將影像縫補(image quilting)的技術應用在補丁的紋理合成(texture synthesis)上，使得縫補的邊緣可以得到較佳的連續性。實驗結果顯示本論文所提出的方法對於增強影像解析度有良好的效果。另外，學習型的超解析度演算法具有資料導向的特性，針對訓練資料的多寡與多樣性對於執行結果的影響，我們也在本論文作進一步的探討。 High-resolution images contain a larger number of pixels, more detailed content and color variations than low-resolution ones. Image resolution enhancement has been an important research area in digital image processing. In this thesis, we developed an example-based super-resolution algorithm which utilizes a collection of reduced-resolution images and their corresponding high-resolution images as examples to guide the estimation of plausible high resolution images from low-resolution ones. Two factors in the algorithm will influence the quality of the output image. One is the method for patch synthesis and the other is the selection of training data. To obtain better continuity among the boundaries between neighboring patches, we apply image quilting technology to synthesize the patch textures. Experimental results show that the proposed method has good performance on sharpening images. In addition, since example-based super resolution is intrinsically data-driven, we will also investigate the influence of the amount and the diversity of the training data on the result.
參考文獻:	[1] S.C. Park, M.K. Park, M.G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 21-36, May. 2003. [2] R.C. Gonzalez and R.E. Woods, “Digital Image Processing,” 2nd edition, Prentice Hall, New Jersey, 2002. [3] H.S. Hou and 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. [4] R.G. Keys, “Cubic Convolution Interpolation for Digital Image Processing,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. ASSP-26, no. 6, pp. 1153-1160, Dec.1981. [5] S. Baker and T. Kanade, “Limits on Super-Resolution and How to Break Them,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1167-1183, 2002. [6] W.T. Freeman and E.C. Pasztor, “Learning to estimate scenes from images,” Advances in Neural Information Processing Systems, vol. 11, pp.775-781, Nov. 1999. [7] W.T. Freeman, E.C. Pasztor and O. T. Carmichael, “Learning low-level vision,” International Journal of Computer Vision, vol. 40, no. 1, pp. 25-47, Oct. 2000. [8] W.T. Freeman, T.R. Jones, and E.C. Pasztor, “Example-based super-resolution,” IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56-65, Mar. 2002. [9] J. Sun, N. N. Zheng, H. Tao, and H.Y. Shum, “Image Hallucination with Primal Sketch Priors,” In IEEE International Conference on Computer Vision and Pattern Recognition, volume 2, pages 729-736, 2003. [10] S. Baker, and T. Kanade, “Hallucinating Faces,” Proceedings IEEE International Conference on Automatic Face and Gesture Recognition, pp. 83-88, 2000. [11] X. Wang and X. Tang, “Face Hallucination and Recognition,” In Proceedings of 4th Int. Conf. Audio and video based Personal Authentication, IAPR, University of Surrey, Guildford, UK, 2003. [12] C. Liu, H.-Y. Shum, and W. T. Freeman, “Face hallucination: Theory and practice,” Int. J. Comput. Vis., vol. 75, no. 1, pp. 115-134, 2007. [13] J. Yang, H. Tang, Y. Ma, and T. Huang, “Face hallucination via sparse coding,” in Proc. IEEE Conf. Image Process., pp.1264-1267, 2008. [14] X. Ma, J. Zhang, and C. Qi, “Hallucinating face by position-patch,” Pattern Recognition, vol.43, no.6, pp.3178-3194, 2010. [15] A. A. Efros and W. T. Freeman, “Image quilting for texture synthesis and transfer,” in Proceedings of SIGGRAPH 2001, pages 341-346, August 2001. [16] L.Y. Wei and M. Levoy, “Fast Texture Synthesis Using Tree-Structured Vector Quantization,” in SIGGRAPH 2000 Conference Proceedings, pages 479-488. 2000. [17] D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,” in Computer Vision, 2009 IEEE 12th International Conference on, pp. 349-356, 2009. [18] R. Y. Tsai and T. S. Huang, “Multiframe Image Restoration and Registration,” Advances in Computer Vision and Image Processing, vol. 1, pp. 317-339, JAI Press, London, 1984. [19] R. R. Schultz, R. L. Stevenson, “A Bayesian approach to image expansion for improved definition,” IEEE Trans. on Image Processing, vol. 3, pp. 233-242, May. 1994. [20] W.T. Freeman and E.C. Pasztor, “Markov networks for super-resolution,” Proc. of the 34th Conference on Information Sciences and Systems, Princeton, New Jersey, U.S.A, Mar. 2000. [21] E. W. Dijkstra, “A note on two problems in connexion with graphs”, Numer. Math. vol. 1, pp. 269-71, 1959. [22] B. Weyrauch, J. Huang, B. Heisele, and V. Blanz, “Component-based Face Recognition with 3D Morphable Models”, First IEEE Workshop on Face Processing in Video, Washington, D.C., 2004. [23] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
描述:	碩士國立政治大學資訊科學學系 96971002 102
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0096971002
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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