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dc.contributor.authorChen, Ting-Chiehen_US
dc.identifier.citation中文: [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, 2007. [2] Alasdair McAndrew原著, 徐曉珮譯, 數位影像處理. 高立圖書有限公司, 2008. 英文: [3] I. Aizenberg, DV. Paliy, and JM. Zurada, “Blur identification by multilayer neural network based on multivalued neurons,” IEEE Transactions on Neural Networks, Vol. 19, No. 5, pp. 883-898. 2008. [4] SD. Babacan, R. Molina, and AK. Katsaggelos, “Parameter estimation in TV image restoration using variational distribution approximation,” IEEE Transactions on Image Processing, Vol. 17, No. 3, pp. 326-339. 2008. [5] L. Bar, N. Kiryati, and N. Sochen, “Image deblurring in the presence of impulsive noise,” International Journal of Computer Vision, Vol. 70, No. 3, pp. 279-298. 2006. [6] JM. Bioucas-Dias, “Bayesian wavelet-based image deconvolution: A GEM algorithm exploiting a class of heavy-tailed priors,” IEEE Transactions on Image Processing, Vol. 15, No. 4, pp. 937-951. 2006. [7] D. Calvetti, and Erkki Somersalo, “A Gaussian hypermodel to recover blocky objects,” Inverse Problems, Vol. 23, No. 2, pp. 733-754. 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] YM. Huang, MK. Ng, and YW. Wen, “A fast total variation minimization method for image restoration,” Multiscale Modeling & Simulation, Vol. 7, No. 2, pp. 774-795. 2008. [10] A. Hyvarinen, and E. Oja, “Independent component analysis:Algorithms and applications,”Neural Networks, pp. 411-130, 2000. [11] D. Li, and R.M Mersereau, and S. Steven, “Blur identification based on kurtosis minimization,”Image Processing, 2005. ICIP 2005. IEEE International Conference. pp. I-905-8, 2005. [12] D. Li, and S. Steven, “Atmospheric turbulence degraded-image restoration by kurtosis minimization,”IEEE Geoscience and remote sensing letters, Vol. 6, No. 2, pp. 244-247, 2009. [13] R. Molina , J. Mateos , and AK. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Transactions on Image Processing, Vol. 15, No. 14, pp. 3715-3727. 2006. [14] H. Yin, and I. Hussain, “ICA and genetic algorithms for blind signal and image deconvolution and deblurring, ” International Conference on Intelligent Data Engineering and Automated Learning, pp. 595-603, 2006. [15] H. Yin, and I. Hussain, “Blind source separation and genetic algorithm for image restoration,”Advances in Space Technologies, 2006 International Conference, pp. 167-172, 2006. [16] H. Yin, and I. Hussain, “Independent component analysis and nongaussianity for blind image deconvolution and deblurring,” Integrated Computer-Aided Engineering, pp. 219-228, 2008. [17] L. Yuan, J. Sun, and L. Quan, “Image deblurring with blurred/noisy image pairs,” ACM Transactions on Graphics, Vol. 26, No. 3, pp. 1-10. 2007.zh_TW
dc.description.abstract隨著數位影像的使用日益普及,模糊影像還原技術的優劣也愈來愈受到重視。造成影像模糊的原因,例如受限於相機本身對焦能力,所造成鏡頭的失焦等等,然而諸如此類的原因,可能是單一個或同時多個因素所造成,因此要怎麼把這些被破壞的影像重建回來的相關研究,便油然而生。 大部份現有的影像還原方法都需要依賴事前的資訊,例如假設遭受破壞過程的資訊是已知,故還原效果的好壞,需視所給予的事前資訊多寡而定;然而,在現實生活中,這些資訊往往是無法事先得知的。 本篇論文將獨立成份分析(Independent Component Analysis,ICA)的技術應用在模糊影像還原上,利用其能夠將混合訊號中的每一個獨立訊號分離的特性,來估測模糊函數(Point Spread Function,PSF);相較於傳統方法為了估測模糊函數而使用大量的迭代,使得運算過程的時間複雜度提高,並且可能有不穩定的情況發生;論文中提到的方法運算方式簡易,且不需要太多的事前資訊,從實驗結果可以觀察到與現有的方法比較,呈現了一定的水準之上,加上使用影像區塊的處理方式,更加強了運算過程的穩定性。zh_TW
dc.description.abstractDeblurring is a challenging problem in many digital image processing applications. The sources of degradation can be misfocus, for example many images suffer from lens out of focus blur because of manufacturing limitations etc. A digital image can suffer blurring from a single or a combination of various PSFs(Point Spread Functions). Therefore the blur image restoration becomes a popular issue. Most existing methods require certain knowledge about the prior information, for example the blurring process of image, and their effect depends on the amount of prior information obtained. However, the prior information is usually unknown. Independent component analysis(ICA) is a useful method for recovering signals from their mixtures and can be applied for finding PSF in this paper. Often an iterative procedure is required for estimating the PSF and is computational complex and expensive, also, it is sometimes instable. The method proposed in this thesis is simple and does not require prior information regarding the blurring process. The proposed method has been tested on degraded images and the results are compared to those of existing methods. Experimental results show that the proposed method outperforms some other methods and shows good stability.en_US
dc.description.tableofcontents摘要 i Abstract ii 目次 iii 表目次 v 圖目次 vi 第一章 緒論 1 1.1 簡介 1 1.2 論文架構 2 第二章 知識背景及相關研究 3 2.1 模糊函數 3 2.2 盲影像還原 3 2.3 相關研究 4 2.3.1 機率論原理 5 2.3.2 適用特定PSF的方法 5 2.3.3 估測PSF的參數 6 第三章 實作方法 7 3.1 獨立成份分析 7 3.2 獨立訊號的非高斯特性 8 3.3 Kurtosis 8 3.4 獨立成份分析與影像模糊的關係 9 3.4.1 不同模糊程度下的kurtosis分佈 10 3.4.2 非高斯分析 12 3.5 影像特性分析 15 3.6 影像區塊選擇 17 3.7 PSF類型 21 3.8 執行程序 23 第四章 實驗結果與分析 26 4.1 實驗環境 26 4.2 實驗結果及分析 26 第五章 結論與未來發展 46 5.1 結論 46 5.2 未來發展 46 參考文獻 47zh_TW
dc.titleImage Restoration Using Independent Component Analysis on a Specific Regionen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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