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研究生:關永輝
研究生(外文):Yong-Hooi Kong
論文名稱:基於限制性最小化平方濾波及邊緣偵測之失焦模糊影像還原
論文名稱(外文):Out-of-focus Image Restoration Based on Constrained Least Square Filter and Edge Detection
指導教授:孫宗瀛孫宗瀛引用關係
指導教授(外文):Tsung-Ying Sun
學位類別:碩士
校院名稱:國立東華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
論文頁數:95
中文關鍵詞:隱蔽影像還原邊緣偵測粒子群最佳化限制性最小化平方濾波點擴散函數
外文關鍵詞:blind image restorationedge detectionparticle swarm optimizationconstrained least square filterpoint spread function
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  • 被引用被引用:1
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  • 下載下載:59
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隱蔽影像還原可以分為兩個步驟來處理,在模糊影像資訊未知的情況,鑑別模糊影像的點擴散函數,接著利用好的影像反摺積演算法還原模糊影像。基於限制性最小化平方濾波及邊緣偵測,本論文提出智慧型搜尋演算法,鑑別失焦影像中的點擴散函數與影像反摺積最佳參數。
本論文針對失焦影像進行研究,利用粒子群最佳化演算法來鑑別未知的點擴散函數與限制性最小化平方濾波的最佳α,再將鑑別後的點擴散函數與濾波的最佳α利用限制性最小化平方濾波還原影像,使得失焦的影像恢復清晰。本論文以不同邊緣評估方式設計目標函數,藉由目標函數決定粒子群的移動搜尋方向,當粒子群收斂後,即可鑑別出點擴散函數與限制性最小化平方濾波之α進行失焦影像還原。
本論文經實驗證明,本演算法不僅可以鑑別出失焦模糊影像還原為清晰影像的點擴散函數,也可以同時鑑別出限制性最小化平方濾波之α,並且限制性最小化平方濾波可以獲得相當不錯的還原品質。
Blind image restoration can be divided into two working processes: identifies point spread function (PSF) of the blurred image without knowing any of its information, following by the usage of an effective image deconvolution algorithm. Based on constrained least square filter and edge detection, a smart searching algorithm is proposed to identify the PSF of blurred image and optimal parameter of image deconvolution.
Aiming to study out-of-focus image, Particle Swarm Optimization (PSO) algorithm is used to distinguish unknown PSF and the optimum parameter of constrained least square filter, subsequently restoring image by the utilisation of constrained least square filter on identified PSF and the optimal parameter. Furthermore, different edge evaluation methods in creating objective function are suggested to determine the mobile searching direction of particle swarm, once particles converged, PSF and parameter of filter can be identified, hence restoring out-of-focus image.
This algorithm has been proven with a series of experiments, showing the advantage of identifying the PSF in out-of-focus image restoration and the parameter of constrained least square filter simultaneously, as well as achieving excellent restoration quality in constrained least square filter.
摘要 ..................................................... I
ABSTRACT..................................................II
誌謝 ................................................... III
目錄 .................................................... IV
圖目錄.................................................... VI
表目錄.................................................. VIII
第一章緒論 .................................................1
1-1 前言.........................................................1
1-2 文獻回顧................................................3
1-3 研究動機................................................6
1-4 論文架構................................................7
第二章隱蔽影像原理及相關理論....................................9
2-1 隱蔽影像反摺積問題........................................9
2-1-1 隱蔽影像摺積模型........................................9
2-1-2 點擴散函數模型....................................... 11
2-1-3 估測模糊函數..........................................15
2-2 影像反摺積演算法.........................................17
2-2-1 Richardson-Lucy ....................................17
2-2-2 Wiener濾波器.........................................18
2-2-3 限制性最小化平方濾波...................................19
2-2-4 還原影像之分析........................................21
2-3 相關理論...............................................28
2-3-1 粒子群最佳化演算法.....................................28
2-3-2 影像與邊緣的關係.......................................32
2-3-2-1 Haar小波轉換.......................................33
2-3-2-2 Roberts邊緣偵測....................................36
2-3-2-3 邊緣數量評估........................................38
2-3-2-4邊緣熵(Entropy)評估.................................38
2-3-2-5 失焦模糊影像之Haar小波轉與Roberts邊緣偵測換測試.........40
第三章失焦影像還原演算法......................................43
3-1 粒子群最佳化應用於估測失焦模糊模型與限制性最小化平方濾波之α ....43
3-2 目標函數的設計..........................................45
3-2-1 目標函數的訂定........................................45
3-3 演算法流程.............................................46
第四章實驗模擬..............................................51
4-1 實驗說明...............................................51
4-2 實驗結果...............................................54
4-2-1 模擬失焦影像之還原.....................................54
4-2-2 模擬文獻[33]失焦影像之還原..............................67
4-3 實驗討論...............................................75
第五章結論與未來工作.........................................77
5-1 結論..................................................77
5-2 未來工作...............................................78
參考文獻...................................................79
作者簡歷...................................................83
[1] M. R. Banham and A. K. Katsaggelos, “Digital image restoration,” IEEE Journal of Signal Processing Magazine, vol.14, no.2, pp.24-41, Mar. 1997.
[2] T. G. Stockham, T. M. Cannon and R. B. Ingebretsen, “Blind deconvolution through digital signal processing,” Journal of Proceeding of IEEE , vol. 63, no. 4, pp. 678- 692, Apr. 1975.
[3] T. M. Cannon, “Blind deconvolution of spatially invariant image blurs with phase,” IEEE Trans. on Acoustics, Speech and Signal Processing, vol. 24, no. 1, pp. 58- 63, Feb. 1976.
[4] J. Biemond, F. G. van der Putten and J. W. Woods, “Identification and restoration of images with symmetric noncausal blurs,” IEEE Trans. on Circuits and System, vol. 35, no. 4, pp. 385-393, Apr. 1988.
[5] R. G. Lane and R. H. T. Bates, “Automatic multidimensional deconvolution,” Journal of Optical Society of America, vol.4, no.1, pp. 180-188, 1987.
[6] G. R. Ayers and J. C. Dainty, “Iterative blind deconvolution method and its applications,” Journal of Optical Letters of America, vol. 13, no. 7, pp. 547-549, 1988.
[7] R. L. Lagendijk, J. Biemond and D. E. Boekee, “Blur identification using the expectation-maximization algorithm,” in Proc. International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp.1397-1400, Glasgow, Scotland, pp. 23-26 May 1989.
[8] Stanley J. Reeves and Russell M. Mersereau, “Identification of image blur parameters by the method of Generalized Cross-validation,” in Proc. IEEE International Symposium on Circuits and Systems, vol. 1, pp. 223–226, 1990.
[9] V. Z. Mesarovic, N. P. Galatsanos and A. K. Kastsaggelos, “Regularized Constrained Total Least Squares Image Restoration,” IEEE Trans. Image Processing, vol. 4, no. 8, pp. 1096-1180, 1995.
[10] T. F. Chan and C. K. Wong, “Total Variation Blind Deconvolution,” IEEE Trans. Image Processing, vol. 7, no. 3, pp. 370-375, 1998.
[11] L. Rudin, S. Osher and E. Fatemi, “Nonlinear Total Variation Based Noise Removal Algorithm,” Physica D, vol. 60, pp. 259-268, 1992.
[12] Lei Liang and Yuanchang Xu, “Adaptive Landweber method to deblur images,” IEEE Signal Processing Letters, vol.10, no.5, pp. 129-132, May 2003.
[13] J. Biemond, R. L. Lagendjik, and R. M. Mersereau, “Iterative methods for image deblurring,” Proceeding of IEEE, vol. 78, pp. 856–883, May 1990.
[14] A. C. Likas and N. P. Galatsanos, “A variational approach for Bayesian blind image deconvolution,” IEEE Trans. on Signal Processing, vol.52, no.8, pp. 2222- 2233, Aug. 2004.
[15] I. M. Qureshi, A. Jalil, and A. Naveed, “Space-variant Neural Network Approach to Blind Image Deconvolution,” in Proc. IEEE Multitopic Conference, pp. 120–127, Dec. 2006.
[16] Yu He, Kim-Hui Yap, Li Chen, and Lap-Pui Chau, “A Novel Hybrid Model Framework to Blind Color Image Deconvolution,” IEEE Trans. on Systems, Man and Cybernetics, vol. 38, no. 4, pp.867-880, July 2008.
[17] T. Y. Sun, S. J. Ciou, C. C. Liu and C. L. Huo, “Out-of-focus blur estimation for blind image deconvolution: Using particle swarm optimization,” in Proc. IEEE International Conference on Systems, Man and Cybernetics, pp.1627-1632, San Antonio, Texas, USA, 11-14 Oct. 2009.
[18] P. D. Samarasinghe, “Blind deconvolution of natural images using segmentation based CMA,” in Proc. International Conference on Signal Processing and Communication Systems, pp.1-7, Dec. 2010.
[19] M. S. C. Almeida and L. B. Almeida, “Blind and Semi-Blind Deblurring of Natural Images,” IEEE Trans. on Image Processing, vol.19, no.1, pp.36-52, Jan. 2010.
[20] T. Y. Sun, Y. C. Lai, C. L. Huo, and Y. H. Yu “PSO-based estimation for Gaussian blur in blind image deconvolution problem,” in Proc. IEEE International Conference on Fuzzy Systems, pp.1143-1148, Taipei, Taiwan, June 2011.
[21] F. Sroubek and P. Milanfar, “Robust Multichannel Blind Deconvolution via Fast Alternating Minimization,” IEEE Trans. on Image Processing, vol. 21, no. 4, pp.1687-1700 April 2012.
[22] 邱信哲,基於邊緣評估之失焦影像還原演算法,國立東華大學98學年度碩士論文。
[23] Michael Cannon, “Blind Deconvolution of Spatially Invariant Image Blurs with Phase,” IEEE Trans. on Acoustics, Speech and Signal Processing, vol. ASSP-24, no. 1, pp. 58-63 Feb. 1976.
[24] A. E. Savakis and H. J. Trussell, “On the Accuracy of PSF Representation in Image Restoration,” IEEE Trans. on Image Processing, vol. 2, no. 2, pp. 252-259, Apr. 1993.
[25] R. E. Hufnagel and N. R. Stanley, “Modulation Transfer Function Associated with Image Transmission through Turbulence Media,” Optical Society of America Journal A, vol. 54, pp. 52–61, 1964.
[26] R. C. Gonzalz and R. E. Woods, Digital Image Processing, Prentice Hall, 2002.
[27] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis and W. T. Freeman, “Removing Camera Shake from a Single Photograph,” ACM Trans. on Graphics, vol. 25, no. 3, pp. 787–794, Jul. 2006.
[28] W. H. Richardson, “Bayesian-Based Iterative Method of Image Restoration,” Journal of Optical Society of America, vol. 62, no. 1, pp. 55-59, 1972.
[29] L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Journal of the Astronomical, vol. 79 no. 6, pp. 745-754, 1974.
[30] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE International Conference on Neural Networks, vol.4, pp.1942-1948, Perth, Australia, Nov. /Dec. 1995.
[31] A. Haar, “Theorie der orthogonalen Funktionen-systeme,” Mathematische Annalen, vol. 69, pp. 331–371, 1910.
[32] L. G. Roberts, Machine Perception of Three-dimensional Solids, Ph. D. thesis, Massachusetts Institute of Technology, 1963.
[33] 賴垟志,基於邊緣偵測及其離散度之隱蔽影像還原演算法,國立東華大學99學年度碩士論文。
[34] D. G. Luenberger. Linear and Nonlinear Programming. Addison-Wesley, 2nd edition, 1984.
[35] Karin Zielinski and Rainer Laur, “Constrained Single-Objective Optimization Using Particle Swarm Optimization,” in Proc. IEEE Congress on Evolutionary Computation, pp. 443-450, 2006.
[36] Yuhui Shi and Russell Eberhart, “A Modified Particle Swarm Optimizer, ” IEEE International Conference on Evolutionary Computation Proceedings, pp.69-73, 1998.
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