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Volume 31 Issue 7
Dec.  2010
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Sun Kang, Jin Gang, Zhu Xiao-Hua. The Long-range Dependence Characteristic Analysis of Sea Clutter Based on the Semivariogram Function[J]. Journal of Electronics & Information Technology, 2012, 34(10): 2466-2469. doi: 10.3724/SP.J.1146.2011.01341
Citation: Liu Peng, Liu Ding-sheng, Li Guo-qing. Selecting Regularization Parameter in Time Marching Method Based on the Synchronous Iteration of Noise and Image[J]. Journal of Electronics & Information Technology, 2009, 31(7): 1711-1715. doi: 10.3724/SP.J.1146.2008.00582

Selecting Regularization Parameter in Time Marching Method Based on the Synchronous Iteration of Noise and Image

doi: 10.3724/SP.J.1146.2008.00582
  • Received Date: 2008-05-14
  • Rev Recd Date: 2009-01-15
  • Publish Date: 2009-07-19
  • In order to correctly estimate the variance of noise in iteration, a pure synthesis noise as an image is synchronously iterated with the observation image in de-convolution, and it takes variance of pure noise image as the estimation of the variance of noise in observation image and computes the regularization parameter by the variance. A novel regularization term that can ensure the synchronous changing of the variance of the two noises is proposed in this article. The new regularization term is put into use only in iteration of pure noise image. Under the condition of knowing the variance of noise of image in iteration, this paper established the relationship between the variance of synthetic noise and the regularization parameter, and the relationship was converted to a simple quadratic equation. Experiments confirm that the new algorithm not only better restrains the noise but also avoids the over smoothing. The adaptability of total variation based image restoration is improved.
  • Rudin L, Osher S, and Fatemi E. Nonlinear total variationbased noise removal algorithms[J].Physica D.1992, 60(1-4):259-268[2]Vogel C R and Oman M E. Fast, robust total variation-basedreconstruction of noisy, blurred images[J].IEEE Transactionson Image Processing.1998, 7(6):813-824[3]Chan Tony F, Golub Gene H, and Mulet Pep. A nonlinearprimal-dual method for total variation-based imagerestoration[R]. UCLA Math Department CAM Report, 1995.[4]Bouman C and Sauer K. A generalized Gaussian image modelfor edge-preserving MAP estimation[J].IEEE Transactionson Image Processing.1993, 2(3):296-310[5]Charbonnier P, Blanc-Fraud L, Aubert G, and Barlaud M.Deterministic edge-preserving regularization in computerimaging[J].IEEE Transactions on Image Process.1997, 6(2):298-311[6]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[7]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[8]Chan T F and Wong C K. Total variation blinddeconvolution[J].IEEE Transactions on Image Processing.1998, 7(3):370-375[9]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[10]Gilboa G, Sochen N, and Zeevi Y Y. Variational denoising ofpartly-textured images by spatially varying constraints[J].IEEE Transactions on Image Processing.2006, 15(8):2281-2289[11]Mrazek P. Selection of optimal stopping time for nonlineardiffusion filtering[J]. International Journal of ComputerVision, 2003, 52(2/3): 189-203.[12]Golub G H, Heath M, and Wahba G. Generalizedcross-validation as a method for choosing a good ridgeparameter [J].Technometrics.1979, 21(2):215-223[13]Craven P and Wahba G. Smoothing noisy data with splinefunctions-estimating the correct degree of smoothing by themethod of generalized cross validation[J]. NumerischeMathematik, 1979, 31(4): 377-403.[14]Hansen P C and O'Leary D P. The use of the L-curve in theregularization of discrete ill posed problems[J].SIAM Journalof Science Computing.1993, 14(6):1487-1503[15]Hansen P C. Analysis of discrete ill-posed problems by meansof the L-curve[J].SIAM Review archive.1992, 34(4):561-580[16]Archer G and Titterington D. On some bayesianregularization methods for image restoration[J].IEEETransactions on on Image Processing.1995, 4(7):989-995[17]Galatsanos N and Katsaggelos A. Methods for choosing theregularization parameter and estimating the noise variance inimage restoration and their relation[J].IEEE Transactions onon Image Processing.1992, 1(3):322-336[18]Galatsanos N, Mesarovic V, Molina R, Mateos J, andKatsaggelos A. Hyper-parameter estimation using gammahyper-priors in image restoration from partially-knownblurs[J].Optical Engineering.2002, 41(8):1845-1854[19]Molina R, Katsaggelos A, and Mateos J. Bayesian andregularization methods for hyperparameter estimation inimage restoration[J].IEEE Transactions on Image Processing.1999, 8(2):231-246[20]Deng G. Iterative learning algorithms for linear Gaussianobservation models[J].IEEE Transactions on on SignalProcessing.2004, 52(8):2286-2297[21]Morozov V A. On the solution of functional equations by themethod of regularization[J]. Soviet Math. Dokl, 1966, 7(1):414-417.
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