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題名 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
貢獻者 廖文宏
Liao, Wen Hung
郭勝夫
Kuo, Sheng Fu
關鍵詞 超解析度
補丁
影像縫補
紋理合成
super resolution
patch
image quilting
texture synthesis
日期 2013
上傳時間 2-Jan-2014 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
資料類型 thesis
dc.contributor.advisor 廖文宏zh_TW
dc.contributor.advisor Liao, Wen Hungen_US
dc.contributor.author (Authors) 郭勝夫zh_TW
dc.contributor.author (Authors) Kuo, Sheng Fuen_US
dc.creator (作者) 郭勝夫zh_TW
dc.creator (作者) Kuo, Sheng Fuen_US
dc.date (日期) 2013en_US
dc.date.accessioned 2-Jan-2014 14:06:39 (UTC+8)-
dc.date.available 2-Jan-2014 14:06:39 (UTC+8)-
dc.date.issued (上傳時間) 2-Jan-2014 14:06:39 (UTC+8)-
dc.identifier (Other Identifiers) G0096971002en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63212-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 96971002zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 緒論 1
1.1 研究動機與目的 1
1.2 論文架構 5
第二章 相關研究 6
2.1 影像內插法 7
2.1.1 最近相鄰內差法 7
2.1.2 雙線性內插法 8
2.1.3 雙立方內插法 9
2.2 以樣本為基礎的超解析度演算法 10
2.2.1 產生關聯模型的訓練資料庫 11
2.2.2 馬可夫網路模型 11
2.2.3 One-pass演算法 12
2.3 影像縫補應用於紋理合成與轉移 15
2.3.1 影像縫補流程 16
2.3.2 最小錯誤邊界分割 17
第三章 研究方法 19
3.1 訓練程序 19
3.1.1 關聯模型 20
3.1.2 模糊化與重新取樣 21
3.1.3 高頻濾波 22
3.1.4 影像切割與局部對比正規化 23
3.2 超解析度程序 24
3.2.1 影像前處理 24
3.2.2 影像分割與局部對比正規化 25
3.2.3 資料搜尋與局部對比正規化的反向運算 26
3.2.4 影像縫補 27
第四章 實驗結果與分析 33
4.1 實驗環境 33
4.1.1 影像資料庫 33
4.1.2 影像評量方法 39
4.2 實驗結果 40
4.2.1 受測影像的取樣比率對於超解析度結果的影響 41
4.2.2 不同補丁合成方法對於超解析度結果的比較 45
4.3 訓練資料的特性對於超解析度結果的影響 54
4.3.1 不同數量的訓練資料對於超解析度結果的比較 54
4.3.2 不同性別的訓練資料對於超解析度結果的比較 56
4.3.3 不同人種的訓練資料對於超解析度結果的比較 61
4.3.4 不同類別的訓練資料對於超解析度結果的比較 66
第五章 結論與未來方向 70		zh_TW
dc.format.extent 6088044 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096971002en_US
dc.subject (關鍵詞) 超解析度zh_TW
dc.subject (關鍵詞) 補丁zh_TW
dc.subject (關鍵詞) 影像縫補zh_TW
dc.subject (關鍵詞) 紋理合成zh_TW
dc.subject (關鍵詞) super resolutionen_US
dc.subject (關鍵詞) patchen_US
dc.subject (關鍵詞) image quiltingen_US
dc.subject (關鍵詞) texture synthesisen_US
dc.title (題名) 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zh_TW
dc.title (題名) Image Quilting for Example-based Super Resolutionen_US
dc.type (資料類型) thesisen
dc.relation.reference (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.		zh_TW