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几种矢量图像噪声去除变分模型的边缘保持比较

DOI: 10.11834/jig.20111219

Keywords: 矢量图像,变分模型,边缘保持,图像去噪,Split-Bregman算法

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Abstract:

矢量图像噪声去除的变分模型必须考虑不同通道图像间的耦合以保持图像边缘,但所得到的模型复杂、计算效率低,且不同耦合方法对应的模型的边缘保持质量不同。本文首先设计了目前已经提出的这类变分模型的快速SplitBregman算法,然后通过大量数值实验对不同模型的边缘保持特性和计算效率进行了比较。所研究的模型分别使用LTV(layeredtotalvariation)规则项、MTV(multichanneltotalvariation)规则项、CTV(colortotalvariation)规则项、PA(polyakovaction)规则项和RPA(reducedpolyakovaction)规则项。实验结果表明CTV模型对矢量图像去噪边缘保持最好,其他依次是PA模型、MTV模型、RPA模型和LTV模型;LTV模型计算效率最高,其他依次是MTV模型、RPA模型、CTV模型和PA模型。

References

[1]  Weickert J. Theoretical foundations of anisotropic diffusion in image processing [J].Computing Supplement, 1996, 11:221-236.
[2]  Sapiro G, Ringach D L.Anisotropic Diffusion of Multi-valued Images with Applications to Color Filtering [J]. IEEE Transactions on Image Processing, 1996, 5(11): 1582-1586.
[3]  Aujol J F,Kang S H. Color Image Decomposition and Restoration [J]. Journal of Visual Communication and Image Representation, 2006: 17(4): 916-928.
[4]  Yang J, Yin W,Zhang Y. A fast algorithm for edge-preserving variational multichannel image restoration [J]. SIAM Journal on Imaging Sciences, 2009, 2(2):569-592.
[5]  Brook A, Kimmel R,Sochen N. Variational restoration and edge detection for color images [J]. J. Math. Imaging Vision, 2003, 18:247-268.
[6]  Bar L, Brook A, Sochen N, et al. Deblurring of color images corrupted by impulsive noise [J].IEEE Trans. Image Process,2007, 16(4):1101-1111.
[7]  Kaftory R, Sochen N,Yehushua Y Z. Variational blind deconvolution of multi-channel images [J]. IJIST, 2005, 1(15):56-63.
[8]  Chambolle A. An algorithm for total variation minimization and applications [J]. J. Math. Imag. Vis., 2004, 20: 89-97.
[9]  Goldstein T, Osher S. The Split Bregman algorithm for L1 regularized problems [J]. SIAM Journal on Imaging Sciences, 2009, 2(2):323-343.
[10]  Wang Y, Yang J, Yin W, et al. A new alternating minimization algorithm for total variation image reconstruction [J] SIAM Journal on Imaging Sciences, 2008, 1(3):248-272.
[11]  Osher S, Burger M, Goldfarb D, et al. An iterative regularization method for total variation-based image restoration [J]. SIAM Journal on Multiscale Modeling and Simulation, 2005, 4(2):460-489.
[12]  Rudin L, Osher S,Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica D, 1992, 60(1-4):259-268.
[13]  Sochen N, Kimmel R,Malladi R. A general framework for low level vision [J]. IEEE Transactions on Image Processing, Special Issue on PDE based Image Processing, 1998, 7(3):310-318.
[14]  Blomgren P,Chan T F. Color TV: Total variation methods for restoration of vector-valued images [J]. IEEE Trans. Image Processing, 1998, 7:304-309.
[15]  Bresson X,Chan T F. Fast dual minimization of the vectorial total variation norm and applications to color image processing [J]. Inverse Problems and Imaging, 2008, 2:455-484.
[16]  Duval V, Aujol J F,Vese L, A Projected Gradient Algorithm for Color Image Decomposition[R/OL].[2011-04-03].http://hal.archives-ouvertes.fr/hal-00292898.

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