Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19926
DC FieldValueLanguage
dc.contributor吳俊霖zh_TW
dc.contributor.author陳君實zh_TW
dc.contributor.authorChen, Chun-Shihen_US
dc.contributor.other資訊科學與工程學系所zh_TW
dc.date2012en_US
dc.date.accessioned2014-06-06T07:07:58Z-
dc.date.available2014-06-06T07:07:58Z-
dc.identifierU0005-2407201217001500en_US
dc.identifier.citation[1] W. H. Richardson, “Bayesian-based iterative method of image restoration,” JOSA, Vol.62, No.1, pp. 55–59, 1972. [2] K. Subr, C. Soler, and F. Durand, “Edge-preserving multiscale image decomposition based on local extrema,” in SIGGRAPH Asia, Singapore, 2008. [3] A. Levin, R. Fergus, F. Durand, and W.T. Freeman, "Image and depth from a conventional camera with a coded aperture", ACM Trans. Graph., Vol.26, No.3, 2007. [4] A. Levin, Y. Weiss, F. Durand, and W.T. Freeman, "Understanding and evaluating blind deconvolution algorithms", Proc. CVPR, pp.1964-1971, 2009. [5] A. Levin, "Blind Motion Deblurring Using Image Statistics", Proc. NIPS, pp.841-848, 2006. [6] D. Kundur and D. Hatzinakos. "Blind image deconvolution", IEEE Signal Processing Magazine, Vol.13, No.3, pp. 43–64, 1996. [7] R. Fergus, B. Singh, A. Hertzmann, S.T. Roweis, and W.T. Freeman, "Removing camera shake from a single photograph", ACM Trans. Graph., Vol.25, No.3, pp.787-794, 2006. [8] R. C. Gonzalez and R. E. Woods, Digital Image Processing (2nd Edition), Prentice Hall, 2002. [9] Y. Wang, H. Feng, Z. Xu, Q. Li, and C. Dai, "An improved Richardson–Lucy algorithm based on local prior", Optics & Laser Technology, Vol.42, No.5, pp. 845–849, 2010. [10] L. Yuan, J. Sun, L. Quan, and H. Shum, "Image deblurring with blurred/noisy image pairs", ACM Trans. Graph., Vol.26, No.3, 2007. [11] S. Zhuo, D. Guo, and T. Sim, "Robust flash deblurring", in Proc. CVPR, pp.2440-2447, 2010. [12] Q. Shan, J. Jia, and A. Agarwala, "High-quality motion deblurring from a single image", ACM Trans. Graph. , Vol.27, No.3, 2008. [13] L. Xu, and J. jia, "Two-Phase Kernel Estimation for Robust Motion Deblurring", Lecture Notes in Computer Science, 2010, Volume 6311, Computer Vision – ECCV 2010, Pages 157-170.en_US
dc.identifier.urihttp://hdl.handle.net/11455/19926-
dc.description.abstract隨著數位相機的普及,對於數位影像的處理成為眾所矚目的焦點,而在一般使用者拍攝數位影像時最容易出現的問題之一便是影像的動態模糊(Motion blur),因此如何有效解決動態模糊的問題也成為主要的研究方向之一,這類研究通稱為影像去模糊(Image deblur)。 在動態模糊中,最常發生的一種情形便是使用者在拍攝數位影像時因為手部不穩的晃動而造成拍攝出來的數位影像有模糊的情形,此類問題所產生的模糊影像可以看作是影像和相機晃動的軌跡進行卷積(Convolution)運算所產生的結果,而解決這類問題的方法一般稱之為反卷積(Deconvolution),其中最為人所知的反卷積方法之一便是Richardson-Lucy 演算法[1](Richardson-Lucy algorithm),Richardson-Lucy演算法能有效地將模糊影像還原為清晰影像,不過所還原出的影像中常有不被預期的瑕疵產生,其中又以漣波(Ringing)最常見,因此解決漣波產生的問題便是許多研究的主要目的。 本篇研究中會以Richardson-Lucy演算法為基礎,透過屬於區域極值濾波器的EMD (Extrema-based multiscale decomposition)[2]對漣波產生的區域及強度作預測,應用在Richardson-Lucy演算法中達到還原清晰影像並且抑制漣波產生。zh_TW
dc.description.abstractWith the popularity of digital camera, digital image processing is getting more important. One of the most common problems in digital photographing is motion blur. The research in solving the problem of motion blur efficiently is called motion deblur. When taking a photograph, the shaking of camera is the reason causing blurred image. The blur process can be formulated as the image takes convolution operation with the shaking path, which is also known as point spread function. One of the well-known deconvolution algorithms in solving the convolution problem is Richardson-Lucy algorithm. Although Richardson-Lucy can recover the image from blurred image, there is unexpected ringing artifact in the deblurred image. To solve ringing is the main purpose in recent researches. In our research, by pre-detecting the region and intensity of ringing in the image, we propose an improved Richardson-Lucy algorithm to deblur image and suppress the ringing.en_US
dc.description.tableofcontents摘要 ii Abstract iii 目錄 iv 圖表目錄 v 第一章 緒論 1 1.1 研究背景及目的 1 1.2 論文架構 3 第二章 文獻探討 4 2.1 Richardson-Lucy演算法 4 2.2 Blurred/noisy影像對去模糊方法 7 2.3 Blurred/flash影像對去模糊方法 9 2.4 Local prior和模糊影像的研究 10 2.5 基於local prior改良的Richardson-Lucy演算法 12 第三章 研究方法 15 3.1 以EMD為基礎之edge map取得 16 3.2 以EMD為基礎的edge map及local prior所改良Richardson-Lucy演算法 21 第四章 實驗結果及討論 26 第五章 結論與未來展望 39 參考文獻 40zh_TW
dc.language.isozh_TWen_US
dc.publisher資訊科學與工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2407201217001500en_US
dc.subject動態模糊zh_TW
dc.subjectMotion bluren_US
dc.subject反卷積zh_TW
dc.subjectRichardson-Lucyzh_TW
dc.subject漣波zh_TW
dc.subjectEMDzh_TW
dc.subjectDeconvolutionen_US
dc.subjectRichardson-Lucyen_US
dc.subjectRingingen_US
dc.subjectEMDen_US
dc.title一個使用區域極值濾波器之改良Richardson-Lucy單張影像去模糊演算法zh_TW
dc.titleAn Improved Richardson-Lucy Algorithm for Single Image Deblurring Using Local Extrema Filteringen_US
dc.typeThesis and Dissertationzh_TW
Appears in Collections:資訊科學與工程學系所
文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.