Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/17945
標題: 以參數化區塊奇異值分解做壓縮和去除雜訊
Parameterized Block-Based Singular Value Decomposition for Image Compression and Denoising
作者: 莊佳芸
Chuang, Chia-Yun
關鍵字: Singular value decomposition
奇異值分解
image compression
image denoising
影像壓縮
影像去雜訊
出版社: 應用數學系所
引用: [1] S.O. Aase, J.H. Husoy and P. Waldemar, ”A Critique of SVD-Based Image Coding Systems,” IEEE International Symposium on Circuits and Systems VLSI,Vol. 4, pp. 13-16, 1999. [2] D. Achlioptas and F. Mcsherry, ”Fast computation of low-rank matrix approximations,” Journal of the ACM, Vol.54(2), 2007. [3] James Chen, Image Compression with SVD, ECS 289K Scientific Computation,2000. [4] D.L. Donoho, ”De-noising by soft-thresholding,” IEEE Trans. Inform. Theory,Vol. 41, pp. 613-627, 1995. [5] G.H. Golub and C.F. VanLoan, Matrix Computations, John Hopkins Univ. Press,1983. [6] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice Hall, 2002. [7] R.C. Gonzalez, R.E. Woods and S.L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall, 2004. [8] R. Karkarala and P.O. Ogunbona, Signal Analysis Using a Multiresolution Form of the Singular Value Decomposition, IEEE Transactions on Image Processing, Vol. 10(5), pp. 724-735, 2001 . [9] V.C. Klema and A.J. Laub, The singular value decomposition: Its computation and, Automatic Control, IEEE Transactions, Vol.25(2), pp. 164- 176 ,1980. [10] K. Konstantinides and K. Yao, Statistic alanalysis of effective singular values in matrix rank determination, IEEE Trans.Acoust.,Speech,Signal Processing, pp. 757-763, 1988. [11] K. Konstantinides, B. Natarajan and G.S. Yovanof, Noise estimation and filtering using block-based singular value decomposition, IEEE Transactions on Image Processing , Vol.6(3), pp. 479-483, 1997. [12] H.-C. Lee et al, Digital image noise suppression method using SVD block transform, U.S. Patent 5010504, 1991. [13] B.K. Natarajan, Filtering random noise from deterministic signal via data compression, IEEE Trans. Signal Processing, Vol.43, pp. 2595-2605, 1995. [14] N. Sae-bae and S. Udomhunsakul, Adaptive Block-Based Singular Value Decomposition Filtering,4th International Conference on Computer Graphics, Imaging and Visualization [CGIV 2007], pp. 298-30, 2007. [15] K. Sayood, Introduction to data compression, Morgan Kaufmann Series in Multimedia Information and Systems, 2000. [16] G.W. Stewart, Perturbation Theory for the Singular Value Decomposition,Computer Science Technical Report Series, Vol. CS-TR-2539, 1990. [17] G.W. Stewart, On the Early History of the Singular Value Decomposition,SIAM Review, Vol.35(4), pp. 551-566, 1990. [18] Mei Tian, Si-Wei Luo and Ling-Zhi Liao, An Investigation into using Singular Value Decomposition as a method of Image Compression, IEEE Conference,Vol.8 , pp. 5200- 5204, 2005. [19] Y. Wongsawat, K.R. Rao and S. Oraintara, Multichannel SVD-Based Image De-Noising, IEEE International Symposium, Vol.62, pp. 5990- 5993, 2005. [20] Jieping Ye, Generalized low rank approximations of matrices, Proceedings of the twenty-first international conference on Machine learning, 2004.
摘要: 一些傳統性的濾波器會使得影像的細節資訊模糊化,在這個論文我們提出兩個以區塊SVD 為基礎的方法。它同時可以用於影像壓縮以及雜訊去除。此方法主要是使用限制每個區塊的PSNR 值將高頻資訊予以適當的去除,並以SVD 型態儲存,以便將來影像重建。為了要得到更好的影像重建效果,我們再提出將傳統的平滑式濾波器與我們所提出的以SVD 為基礎之濾波器作結合。經由實驗結果發現,平滑式濾波器和SVD 濾波器混合使用會比原先個別濾波器有更好的結果,不但能保有影像細節並且有效去除高斯雜訊。
Some traditional filtering techniques blur the image. In order to have a good image quality for preserving the edge structure, we propose two variants of block-based singular value decomposition (BSVD) filters. We exploit that a proper selection of the PSNR parameter on each block can appropriately eliminate the high-frequency information. It is not only used for image compression, but also denoising. Numerical experiments show the great promise in our proposed methods that effectively remove the noise and preserve the edge information.
URI: http://hdl.handle.net/11455/17945
其他識別: U0005-0307200819233500
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0307200819233500
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