Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19722
標題: 利用銳利邊區塊與綜合濾波器還原模糊影像
Image Deblurring Using Sharp Edge Regions and Integrated Filters
作者: 黃俊澤
Huang, Chun-Tse
關鍵字: sharp edge region
綜合濾波器
integrated filters
point spread function
blind deconvolution
點擴散函數盲目反迴旋積
出版社: 資訊科學與工程學系所
引用: [1] Gonzalez, and Woods原著, 繆紹綱譯, 數位影像處理, 普林斯頓國際有限公司, 2008. [2] R. C. Gonzalez, R. E. Woods, and S. L. Eddins原著, 繆紹綱譯, 數位影像處理:運用MATLAB, 東華書局, 2005. [3] M. Ben-Ezra, and S. K. Nayar, “Motion-based motion deblurring,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 6, pp. 689-698, 2004. [4] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, 1986. [5] S. Cho, and S. Lee, “Fast motion deblurring,” ACM Transactions on Graphics, Vol. 28, No. 5, pp. 1-8, 2009. [6] S. Dai, and Y. Wu, “Motion from blur,” Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008. [7] W. Fawwaz A. M, T. Shimahashi, M. Matsubara, and S. Sugimoto, “Psf estimation and image restoration for noiseless motion blurred images,” Proceedings of 2007 WSEAS Conference on Signal, Speech and Image Processing, pp. 1-7, 2007. [8] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” ACM Transactions on Graphics, Vol. 25, No. 3, pp. 787-794, 2006. [9] J. Jia, “Single image motion deblurring using transparency,” Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007. [10] N. Joshi, R. Szeliski, and D. J. Kriegman, “Psf estimation using sharp edge prediction,” Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008. [11] F. Krahmer, Y. Lin, B. McAdoo, K. Ott, J. Wang, D. Widemannk, and B. Wohlberg, “Blind image deconvolution:motion blur estimation,” Technical Report, University of Minnesota, 2006. [12] J.-H. Lee, and Y.-S. Ho, “Non-blind image deconvolution with adaptive regularization,” Technical Report, Gwangju Institute of Science and Technology, 2010. [13] A. Levin, “Blind motion deblurring using image statistics,” Advances in Neural Information Processing Systems, pp. 1-8, 2006. [14] A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Transactions on Graphics, Vol. 26, No. 3, pp. 1-10, 2007. [15] A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1964-1971, 2009. [16] Y.-M. Lin, “Image motion deblurring using bi-level regions,” Master Thesis, Department of Computer Science, National Tsing Hua University, 2008. [17] V. B. S. Prasath, and A. Singh, “Ringing artifact reduction in blind image deblurring and denoising problems by regularization methods,” Proceedings of 2009 IEEE Conference on Advances in Pattern Recognition, pp. 333-336, 2009. [18] Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” ACM Transactions on Graphics, Vol. 27, No. 3, pp. 1-10, 2008. [19] L. A. Shepp, and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Transactions on Medical Imaging, Vol. 1, No. 2, pp. 113-122, 1982. [20] Muhammad Umar Al and Maliki Bin Saifuddin, “Image deblurring(barcode),” Technical Report, Department of Electronic Engineering, University Technology Malaysia, 2010. [21] L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Image deblurring with blurred/noisy image pairs,” ACM Transactions on Graphics, Vol. 26, No. 3, pp. 1-10, 2007. [22] L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Progressive inter-scale and intra-scale non-blind image deconvolution,” ACM Transactions on Graphics, Vol. 27, No. 3, pp. 1-10, 2008. [23] Steve on Image Processing, http://blogs.mathworks.com/steve/, 2007. [24] Gaussian blur, http://en.wikipedia.org/wiki/Gaussian_blur/, 2010. [25] MATLAB, http://www.mathworks.com/, 2010.
摘要: 在拍照時,可能會因對焦不準或相機晃動或物體移動等因素,造成影像失焦或動態模糊,所以模糊影像清晰化的處理是一個重要的研究主題。 本篇論文主要目的就是要將單張失焦或動態模糊的影像還原成清晰的影像。因此,我們提出了利用銳利邊區塊與綜合濾波器還原模糊影像的方法。首先使用邊緣偵測的方法自動尋找此張模糊影像中數個具有銳利邊的區塊,來幫助我們能更準確的估測出點擴散函數(Point Spread Function,PSF)。然後再經由綜合濾波器的運算來得到預設的PSF,接著透過預設的PSF推導出模糊影像區塊的還原影像並與綜合的模糊影像區塊來得到初始的PSF,再利用盲目反迴旋積演算法將模糊影像區塊與初始的PSF反覆的做估算以得到區塊的PSF,直到所有模糊影像區塊都處理完畢,然後將所得到的多個模糊影像區塊之PSF進行傅立葉轉換取得平均值之後,再透過反傅立葉轉換來得到最後估測的PSF。在估測出點擴散函數之後,我們使用非盲目反迴旋積演算法來對整張模糊影像進行影像還原。 我們以實際拍攝的單張失焦或動態模糊生活照為例,經由實作結果證明,本篇論文所提出的影像去模糊方法能有效地將模糊影像還原成清晰影像。
URI: http://hdl.handle.net/11455/19722
其他識別: U0005-2601201110465300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2601201110465300
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