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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均 | |
| dc.contributor.author | Yu Chen | en |
| dc.contributor.author | 陳愈 | zh_TW |
| dc.date.accessioned | 2021-06-16T13:42:20Z | - |
| dc.date.available | 2016-07-19 | |
| dc.date.copyright | 2013-07-19 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-11 | |
| dc.identifier.citation | Reference
Non-blind deconvolution [1] M. Asif, A. Malik, and T.-S. Choi, “3d shape recovery from image defocus using wavelet analysis,” in Image Processing, 2005. ICIP 2005. IEEE International Conference on, vol. 1, pp. I–1025–8, 2005. [2] P.-C. Chen, C.-H. Liu, C.-W. Chang, C.-C. Chang, and L. Angot, “Digital decoding design for phase coded imaging system,” pp. 74431A–74431A–9, 2009. [3] R. Neelamani, H. Choi, and R. Baraniuk, “Forward: Fourier-wavelet regularized deconvolution for ill-conditioned systems,” Signal Processing, IEEE Transactions on, vol. 52, no. 2, pp. 418–433, 2004. [4] M. Sorel and F. Sroubek, “Space-variant deblurring using one blurred and one underexposed image,” in Image Processing (ICIP), 2009 16th IEEE International Conference on, pp. 157–160, IEEE, 2009. [5] C.-T. Shen, W.-L. Hwang, and S.-C. Pei, “Spatially-varying out-of-focus image deblurring with l1-2 optimization and a guided blur map,” in Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pp. 1069– 1072, 2012. [6] A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” in ACM SIGGRAPH 2007 papers, SIGGRAPH ’07, (New York, NY, USA), ACM, 2007. [7] N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson–lucy algorithm with total variation regularization for 3d confocal microscope deconvolution,” Microscopy Research and Technique, vol. 69, no. 4, pp. 260–266, 2006. [8] L. Lucy, “An iterative technique for the rectification of observed distributions,” The astronomical journal, vol. 79, p. 745, 1974. [9] W. H. RICHARDSON, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am., vol. 62, pp. 55–59, Jan 1972. [10] M. Tanaka, K. Yoneji, and M. Okutomi, “High-usability deblurring filter,” in Consumer Electronics, 2008. ISCE 2008. IEEE International Symposium on, pp. 1–2, 2008. [11] R. Xiong, W. Ding, S. Ma, and W. Gao, “A new image deblurring algorithm with less ringing artifacts via error variance estimation and soft decision,” in Image Processing (ICIP), 2011 18th IEEE International Conference on, pp. 701–704, 2011. [12] Y.Wang, J. Yang,W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences, vol. 1, no. 3, pp. 248–272, 2008. [13] D. Krishnan and R. Fergus, “Fast image deconvolution using hyper-laplacian priors,” Advances in Neural Information Processing Systems, vol. 22, pp. 1–9, 2009. [14] P.-C. Chen, Y.-L. Chen, and H.-Y. Sung, “Image restoration based on multiple psf information with applications to phase-coded imaging system,” in Proc. SPIE, vol. 7798, p. 779809, 2010. Blind deconvolution [15] D. Kundur and D. Hatzinakos, “Blind image deconvolution,” Signal Processing Magazine, IEEE, vol. 13, no. 3, pp. 43–64, 1996. [16] W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” in Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 1259–1266, IEEE, 2011. [17] L. Xu and J. Jia, “Depth-aware motion deblurring,” in Computational Photography (ICCP), 2012 IEEE International Conference on, pp. 1–8, IEEE, 2012. [18] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” in ACM SIGGRAPH 2006 Papers, SIGGRAPH ’06, (New York, NY, USA), pp. 787–794, ACM, 2006. [19] J. Bardsley, S. Jefferies, J. Nagy, and R. Plemmons, “Blind iterative restoration of images with spatially-varying blur,” 2005. [20] Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” ACM Trans. Graph., vol. 27, pp. 73:1–73:10, Aug. 2008. [21] L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Progressive inter-scale and intra-scale non-blind image deconvolution,” ACM Trans. Graph., vol. 27, pp. 74:1–74:10, Aug. 2008. Others [22] J. M. Bardsley, “An efficient computational method for total variation-penalized poisson likelihood estimation,” Inverse Problems and Imaging, vol. 2, no. 2, pp. 167–185, 2008. [23] N. Joshi, R. Szeliski, and D. J. Kriegman, “Psf estimation using sharp edge prediction,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1–8, IEEE, 2008. [24] S. Berisha and J. G. Nagy, “Iterative methods for image restoration.” http:// www.mathcs.emory.edu/˜nagy/RestoreTools/. [25] P. C. Hansen, “Rank-deficient and discrete ill-posed problems: Siam,” Philadelphia, PA, pp. 175–206, 1998. [26] Y.W. D. Fan and J. G. Nagy, “Synthetic boundary conditions for image deblurring,” Linear Algebra and Its Applications, vol. 434, no. 11, pp. 2244–2268, 2011. [27] M. Banham and A. Katsaggelos, “Digital image restoration,” Signal Processing Magazine, IEEE, vol. 14, no. 2, pp. 24–41, 1997. [28] M. D. Robinson and D. G. Stork, “Joint design of lens systems and digital image processing,” pp. 63421G–63421G–10, 2007. [29] R. G. Brown, P. Y. Hwang, et al., Introduction to random signals and applied Kalman filtering, vol. 1. John Wiley & Sons New York, 1992. [30] A. N. Tikhonov and A. Leonov, Ill-posed Problems in Natural Sciences: Proceedings of the International Conference Held in Moscow, August 19-25, 1991. Vsp, 1992. [31] D. Geman and G. Reynolds, “Constrained restoration and the recovery of discontinuities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 3, pp. 367–383, 1992. [32] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1-4, pp. 259 – 268, 1992. [33] Y.-W. Tai and M. Brown, “Single image defocus map estimation using local contrast prior,” in Image Processing (ICIP), 2009 16th IEEE International Conference on, pp. 1797–1800, 2009. [34] S. Zhuo and T. Sim, “Defocus map estimation from a single image,” Pattern Recognition, vol. 44, no. 9, pp. 1852–1858, 2011. [35] K. He, J. Sun, and X. Tang, “Guided image filtering,” in Computer Vision–ECCV 2010, pp. 1–14, Springer, 2010. [36] J. M. Ogden, E. H. Adelson, J. R. Bergen, and P. J. Burt, “Pyramid-based computer graphics,” RCA Engineer, vol. 30, no. 5, pp. 4–15, 1985. [37] A. Kubota and K. Aizawa, “Reconstructing arbitrarily focused images from two differently focused images using linear filters,” Image Processing, IEEE Transactions on, vol. 14, no. 11, pp. 1848–1859, 2005. [38] E. W. Weisstein, “Cubic formula.” http://mathworld.wolfram.com/ CubicFormula.html. [39] E. W. Weisstein, “Quartic formula.” http://mathworld.wolfram.com/ QuarticEquation.html. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62342 | - |
| dc.description.abstract | 在這本論文中，我們提出一個對於醫學顯微影像景深拓展系統之點擴散方程式計算與模糊影像重建演算法。利用我們所設計的IDP 影像，理論上IDP 影像可以有效提供所有方向的頻率測量。利用這個特色，我們可以使用真實拍攝的IDP 影像計算出整個顯微影像系統的點擴散方程式。我們提出使用Tikhonov 正規化方法來計算系統的點擴散方程式。當我們得到系統的點擴散方程式後，我們提出一個使用階層式超拉普拉斯L2 空間先驗機率的影像重建方法。藉由這個方法我們可以在影像邊緣部分得到銳利的影像，並且在非邊緣部分得到低雜訊的效果。因為醫學顯微影像比使用一般的相機所照出影像複雜，使用我們的方法也可以應用在一般的數位影像中。 | zh_TW |
| dc.description.abstract | In this thesis, the algorithm for microscopic image deblurring and point spread function (PSF) estimation are proposed. By using the identification pattern that provides measurable frequencies at all orientations, we can estimate
the microscopy system PSF from the actual image. We use the Tikhonov regularization to estimate the PSF of the system. After acquiring the PSF, we use the pyramid Hyper-Laplacian L2 norm priors image reconstruction algorithm to restore blurred medical microscopic images. By using the Hyper- Laplacian and L2 norm priors, we can get sharp reconstruction images and reduce the noise in the smooth region. Since medical microscopic images are much more complicated than the natural images shot by a camera, our algorithm can also be applied in a digital camera system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T13:42:20Z (GMT). No. of bitstreams: 1 ntu-102-R00942049-1.pdf: 11744663 bytes, checksum: 397afee2f964179499d25d7e1bfd47c6 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | Contents
口試委員會審定書i 誌謝iii 中文摘要v Abstract vii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Background Information for Image Deblurring 5 2.1 Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Boundary condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Blur types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.1 Spatially invariant blurs . . . . . . . . . . . . . . . . . . . . . . 9 2.3.2 Spatially variant blurs . . . . . . . . . . . . . . . . . . . . . . . 11 3 Related Works 15 3.1 Spatially-varying out-of-foucs image deblurring with L1-2 optimization and a guided blur map [5] . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Blur map generation . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Image deblurring with L1-2 optimization . . . . . . . . . . . . . 20 3.2 High-usability deblurring filter [10] . . . . . . . . . . . . . . . . . . . . 21 3.2.1 High-usability deblurring filter . . . . . . . . . . . . . . . . . . . 22 3.3 A new image deblurring algorithm with less ringing artifacts via error variance estimation and soft decision [11] . . . . . . . . . . . . . . . . . 23 3.3.1 The estimation problem and iterative solution . . . . . . . . . . . 24 3.3.2 Probabilistic graph model for image deblurring . . . . . . . . . . 25 3.3.3 Prediction and fusion process in belief propagation . . . . . . . . 26 3.3.4 Estimation of error variance . . . . . . . . . . . . . . . . . . . . 26 3.3.5 The propose algorithm . . . . . . . . . . . . . . . . . . . . . . . 28 3.4 Image deblurring using a pyramid-based Richardson–Lucy algorithm . . . 29 3.4.1 Pyramid structure with the RL algorithm . . . . . . . . . . . . . 31 3.4.2 Residual deconvolution . . . . . . . . . . . . . . . . . . . . . . . 34 3.5 Edge-membership based blurred image reconstruction algorithm . . . . . 35 3.5.1 Edge-membership based image reconstruction algorithm . . . . . 36 3.5.2 Reconstructing all focused images from two differently focused images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4 Hyper-Laplacian L2 norm priors image reconstruction algorithm 43 4.1 Residual deconvolution and improved pyramid structure . . . . . . . . . 45 4.1.1 Residual deconvolution using in our structure . . . . . . . . . . . 45 4.1.2 Improved pyramid structure . . . . . . . . . . . . . . . . . . . . 46 4.2 System PSF estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.3 Deblurring method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4 Hyper-Laplacian L2 norm priors image reconstruction algorithm . . . . . 53 4.4.1 lj sub-problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4.2 w sub-problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.5 Summary of the proposed method . . . . . . . . . . . . . . . . . . . . . 58 5 Experimental Results 61 5.1 IDP pattern reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2 USAF test chart reconstruction . . . . . . . . . . . . . . . . . . . . . . . 63 5.3 Other images reconstruction . . . . . . . . . . . . . . . . . . . . . . . . 67 6 Conclusion 83 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Reference 87 | |
| dc.language.iso | en | |
| dc.subject | 編碼影像 | zh_TW |
| dc.subject | 景深 | zh_TW |
| dc.subject | 解模糊 | zh_TW |
| dc.subject | 點擴散方程式計算 | zh_TW |
| dc.subject | Coded imaging | en |
| dc.subject | Depth of field | en |
| dc.subject | Deblurring | en |
| dc.subject | PSF estimation | en |
| dc.title | 醫學顯微影像景深拓展系統之點擴散方程式計算與模糊影像重建演算法 | zh_TW |
| dc.title | Image Reconstruction Algorithms for Medical Microscopic Image Deblurring and Point Spread Function Estimation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許文良,葉敏宏,張銓仲 | |
| dc.subject.keyword | 編碼影像,景深,解模糊,點擴散方程式計算, | zh_TW |
| dc.subject.keyword | Coded imaging,Depth of field,Deblurring,PSF estimation, | en |
| dc.relation.page | 90 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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