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Volume 31 Issue 1
Dec.  2010
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Lu Cheng-wu, Song Guo-xiang. An Image Denoising Method Using Total Variation Regularization for Flow Field[J]. Journal of Electronics & Information Technology, 2009, 31(1): 112-115. doi: 10.3724/SP.J.1146.2007.01039
Citation: Lu Cheng-wu, Song Guo-xiang. An Image Denoising Method Using Total Variation Regularization for Flow Field[J]. Journal of Electronics & Information Technology, 2009, 31(1): 112-115. doi: 10.3724/SP.J.1146.2007.01039

An Image Denoising Method Using Total Variation Regularization for Flow Field

doi: 10.3724/SP.J.1146.2007.01039
  • Received Date: 2007-06-25
  • Rev Recd Date: 2007-11-30
  • Publish Date: 2009-01-19
  • Using image decomposition theory proposed by Meyer, a total variation image denoising method based on smoothing flow field is presented. Firstly, through applying Hilbert-Sobolev norm to measure fidelity term, a total variation filter is used to smooth the normal vectors of the level curves of a noise image. And then, a model is constructed to find a surface which fit smoothed normal vectors. Finally, finite difference schemes are used to solve the Euler-Lagrange functions derived from above models. The experiments show that the approach not only can remove noisy efficiently, but also can retain edges and texture.
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  • Acar R and Vogel C. Analysis of total variation penaltymethods [J].Inverse Problems.1994, 10(6):1217-1229[2]Chambolle A, DeVore R A, and Lee N Y, et al.. Nonlinearwavelet image processing: variational problems, compression,and noise removal through wavelet shrinkage [J].IEEETrans. on Image Processing.1998, 7(3):319-335[3]Chambolle A and Lions P L. Image recovery via totalvariation minimization and related problems[J]. NumerischeMathematik, 1997, 76(2): 167-188.[4]Vese L A and Osher S. Numerical methods for p-harmonicflows and applications to image processing [J].SIAM J.Numer. Anal.2002, 40(6):2085-2104[5]Osher S, Sole A, and Vese L A. Image decomposition andrestoration using total variation minimization and the e H.1norm [J]. Multiscale Modelling and Simulation, 2003, 1(3):1579-1590.[6]Osher S, Burger M, and Goldfarb D, et al.. An iterativeregularization method for total variation based imagerestoration [J].Multiscale Modelling and Simulation.2005, 4(2):460-489[7]Lysaker M, Osher S, and Tai X C. Noise removal usingsmoothed normal and surface fitting [J].IEEE Trans. onImage Processing.2004, 13(10):1345-1357[8]Rudin L I, Osher S, and Fatemi E. Nonlinear total variationbased noise removal algorithms [J].Phys. D.1992, 60(1-4):259-268[9]Meyer Y. Oscillating pattern in image processing andnonlinear evolution equations [M]. Boston: UniversityLecture Series, American Mathematical Society, 2001: 23-78.[10]Tasdizen T, Whitaker R, and Burchard P. Geometric surfaceprocessing via normal maps [J].ACM Trans. Graph.2003,22(4):1012-1033[11]Chan T, Marquina A, and Mulet P. High-order totalvariation-based image restoration [J].SIAM J. Sci. Compu.2000, 22(2):503-516[12]Besl P and Jain R. Segmentation through variable-ordersurface fitting [J].IEEE Trans. on Pattern Anal. MachineIntell.1988, 10(2):167-192
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