The Selection of Optimal Stopping Time Based on Synchronous Iteration of Noise and Image in Diffusion Image De-noise

Liu Peng; Liu Ding-sheng; Li Guo-qing; Li Jing-shan

doi:10.3724/SP.J.1146.2008.01279

Volume 31 Issue 9

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(9): 2084-2087

Liu Peng, Liu Ding-sheng, Li Guo-qing, Li Jing-shan. The Selection of Optimal Stopping Time Based on Synchronous Iteration of Noise and Image in Diffusion Image De-noise[J]. Journal of Electronics & Information Technology, 2009, 31(9): 2084-2087. doi: 10.3724/SP.J.1146.2008.01279

Citation:

Liu Peng, Liu Ding-sheng, Li Guo-qing, Li Jing-shan. The Selection of Optimal Stopping Time Based on Synchronous Iteration of Noise and Image in Diffusion Image De-noise[J]. Journal of Electronics & Information Technology, 2009, 31(9): 2084-2087. doi: 10.3724/SP.J.1146.2008.01279

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The Selection of Optimal Stopping Time Based on Synchronous Iteration of Noise and Image in Diffusion Image De-noise

doi: 10.3724/SP.J.1146.2008.01279

Received Date: 2008-10-06
Rev Recd Date: 2009-04-21
Publish Date: 2009-09-19

Abstract

Abstract

This paper de-noises in image by diffusion filter. And a method of finding optimal stopping time is proposed. The criterion of stopping time is that the variance of image smoothed out is bigger than the variance of noise smoothed out. In order to estimate the variance of noise in iteration correctly, a pure synthesis noise as an image is synchronously iterated with the observation image in iteration, and the variance of pure noise image is taken as the estimation of the variance of noise in estimated image. According to the anisotropy of the regularization, a novel regularization term that can ensure the synchronous changing of the synthesis noise and real noise was proposed in this article. The new regularization term is put into use only in iteration of pure noise image, and the similarity of statistical properties between real noise and synthesis noise can be kept in iteration. Experiment confirms the effectiveness of proposed method to select optimal stopping time.

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References

Perona P and Malik J. Scale-space and edge detection usinganisotropic diffusion[J].IEEE Transactions on PatternAnalysis and Machine Intelligence.1990, 12(7):629-639[2]Rudin L, Osher S, and Fatemi E. Nonlinear Total Variationbased noise removal algorithms[J].Physical D.1992, 60(1-4):259-268[3]Teboul S, Blanc-Feraud L, Aubert G, and Barlaud M.Variational approach for edge-preserving regularization usingcoupled PDE's[J].IEEE Transactions on Image Processing.1998, 7(3):387-397[4]芦碧波, 郭志昌. 非局部偏微分方程去噪模型[J]. 吉林大学学报(理学版), 2007, 45(6): 935-936.Lu Bi-bo and Guo Zhi-chang. Nonlocal partial differentialequation model for image denoising[J]. Journal of JilinUnicersity(Science Edition), 2007, 45(6): 935-936.[5]张红英, 吴亚东, 吴斌. 基于变分PDE 的非线性数字混合滤波器[J]. 计算机辅助设计与图形学学报, 2007, 19(9):1089-1093.Zhang Hong-ying, Wu Ya-dong, and Wu Bin. Nonlineardigital hybrid filter based on variational PDE[J]. Journal ofcomputer-Aided Design Computer Graphics, 2007, 19(9):1089-1093.[6]邵文泽, 韦志辉. 局部几何结构驱动的图像插值放大及超分辨率复原[J]. 中国图象图形学报, 2008, 13(7): 1235-1243.Shao Wen-ze and Wei Zhi-hui.Local geometry driven imagemagnification and super-resolution[J]. Journal of Image andGraphics, 2008, 13(7): 1235-1243.[7]邵文泽, 韦志辉. 基于广义Huber-MRF 图像建模的超分辨率复原算法[J]. 软件学报, 2007, 18(10): 2434-2444.Shao Wen-ze and Wei Zhi-hui. Super-resolution imagerestoration based on Huber-MRF modle[J]. Journal ofSoftware, 2007, 18(10): 2434-2444.[8]Weickert J. Coherence-enhancing diffusion of color images[J].Image and Vision Computing.1999, 17(3):201-212[9]Mrzek P and Navara M. Selection of optimal stoppingtime for nonlinear diffusion filtering[J].International Journalof Computer Vision.2003, 52(2/3):189-203[10]Gilboa G, Sochen N, and Zeevi Y Y. Estimation of optimalPDE-based denoising in the SNR sense[J].IEEETransactions on Image Processing.2006, 15(8):2269-2280[11]Charbonnier P, Blanc-Feraud L, Aubert G, and Barlaud M.Deterministic edgepreserving regularization in computedimaging[J].IEEE Transactions on Image Processing.1997,6(2):298-311[12]Tschumperl D. Vector-valued image regularization withPDEs: A common framework for different applications[J].IEEE Transactions on Pattern Analysis and. MachineIntelligence, 2005, 12(7): 629-639.

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