| [1] | ［1］Pitas I, Venetsanopoulos A-N. Nonlinear digital filters: Principles and applications[M]. Boston: Kluwer Academic, 1990.
|
| [2] | ［2］Astola J. Fundamentals of nonlinear digital filtering[M]. Boca Raton, U.S.A: CRC Press,1997.
|
| [3] | ［4］Justusson B I. Median filtering: Statistical properties[A]. In:Huang H S edi. Two-dimensional digital signal processing, Topics in Applied Physics[M]. Berlin: Springer-Verlag, 1981:161～196.
|
| [4] | ［6］Coyle E J, Lin J H, Gabbouj M. Optimal stack filtering and the estimation and structural approaches to image processing[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989,37(12):2037～2066.
|
| [5] | ［8］Wong E Q, Algazi V R. Image enhancement using linear diffusion and an improved gradient map estimate[A]. In:Proceedings of 1999 IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:154～158.
|
| [6] | ［10］Bouman C, Sauer K. A generalized Gaussian image model of edge preserving map estimation[J]. IEEE Trans. Image Processing, 1993,2(3):296～310.
|
| [7] | ［11］Ching P C, So H C, Wu S Q. On wavelet denoising and its applications to time delay estimation[J]. IEEE Trans. Signal Processing,1999,47(10):2879～2882.
|
| [8] | ［13］Gunawan D. Denoising images using wavelet transform[A]. In:Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing[C]. Victoria BC,USA, 1999:83～85.
|
| [9] | ［15］Lun D P K, Hsung T C. Image denoising using wavelet transform modulus sum[A]. In:Proceedings of the 4th International Conference on Signal Processing[C]. Beijing China,1998:1113～1116.
|
| [10] | ［17］Krishnan S, Rangayyan R M. Denoising knee joint vibration signals using adaptive time-frequency representations[A]. In:Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering \'Engineering Solutions for the Next Millennium[C]. Alberta Canada, 1999:1495～1500.
|
| [11] | ［19］Vidakovic B, Lozoya C B. On time-dependent wavelet denoising[J]. IEEE Trans. Signal Processing, 1998,46(9):2549～2551.
|
| [12] | 更多...
|
| [13] | ［21］Pan Quan, Zhang Pan, Dai Guanzhong et al. Two denoising methods by wavelet transform[J]. IEEE Trans. Signal Processing, 1999,47(12):3401～3406.
|
| [14] | ［23］Krim H, Pesquet J C. On the statistics of best bases criteria[A]. In:Antoniadis A, Oppenheim G edis. Wavelets in statistics of Lecture Notes in Statistics[C]. New York: Springer-Verlag, 1995:193～207.
|
| [15] | ［25］Israel Cohen, Shalom Raz, David Malah. Translation-invariant denoising using the minimum description length criterion[J]. Signal Processing, 1999,75(3):201～223.
|
| [16] | ［27］Bui T D, Chen G. Translation-invariant denoising using multiwavelets[J]. IEEE Trans. Signal Processing, 1998,46(12):3414～3420.
|
| [17] | ［29］Jansen M, Bultheel A. Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise[J]. IEEE Trans. Image Processing, 1999,8(7):947～953.
|
| [18] | ［31］Stein C. Estimation of the mean of a multivariate normal distribution[J]. Annals of Statistics, 1981,9:1135～1151.
|
| [19] | ［33］Krim H, Schick I C. Minimax description length for signal denoising and optimized representation[J]. IEEE Trans. Information Theory, 1999,45(3):898～908.
|
| [20] | ［35］Johnstone I M, Silverman B-W. Wavelet threshold estimators for data with correlated noise[J]. Journal of royal statistics society series(B), 1997,59:319～351.
|
| [21] | ［37］Xu Yansun, Weaver J B, Healy M J et al. Wavelet transform domain filters:A spatially selective noise filtration technique[J]. IEEE Trans. Image Processing, 1994,3(6):743～758.
|
| [22] | ［39］Moulin P, Liu Juan. Analysis of multiresolution image denoising schemes using generalized-Gaussian and complexity priors[J]. IEEE Trans. Information Theory, 1999,45:909～919.
|
| [23] | ［42］Maneesh S, Prakash I, Krishna R et al. Segmentation based denoising using multiple compaction domains[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan,1999:372～375.
|
| [24] | ［45］Bruce A G, Gao Hong Ye. Understanding waveshrink: variance and bias estimation[EB/OL]. http://www.mathsoft.com/wavelets.html.
|
| [25] | ［47］Zhang Xiao Ping, Desai M D. Nonlinear adaptive noise suppression based on wavelet transform[A]. In:Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing[C]. Seatle USA,1998:1589～1592.
|
| [26] | ［49］Mallat S G, Zhang Z F. Matching pursuits with time-frequency dictionaries[J]. IEEE Trans. Signal Processing, 1993,41(12):3397～3415.
|
| [27] | ［51］Prakash I, Moulin P. Multiple-domain image modeling and restoration[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan,1999:362～366.
|
| [28] | ［53］Sezan M I, Stark H. Image restoration by the method of convex projection[J]. IEEE Trans. Medical Imaging, 1982,MI-1(2):95～101.
|
| [29] | ［55］Tekalp A M, Trussell H J. Comparative study of some recent statistical and set-theoretic methods for image restoration[A]. In:Proceedings of IEEE International Conference on Acoustics, speech, signal processing[C]. New York USA, 1988:988～991.
|
| [30] | ［57］Pan Quan, Zhang Lei, Zhang Hongcai et al. Adaptive wavelet based spatially de-noising[J]. In:Proceedings of the 4th International Conference on Signal Processing Proceedings[C]. Beijing China,1998:486～489.
|
| [31] | ［59］Mihcak M K, Kozintsev I, Ramchandran K. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising[A].In:Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing[C]. Phoenix,USA,1999:3253～3256.
|
| [32] | ［62］Coifman R R, Donoho D L. Translation invariant de-noising[A]. In:Wavelets in Statistics of Lecture Notes in statistics 103[C]. New Youk: Springer-Verlag, 1994:125～150.
|
| [33] | ［64］Downie T R, Silverman B W. The discrete multiple wavelet transform and thresholding methods[J]. IEEE Trans. Signal Processing, 1998,46(9):2558～2561.
|
| [34] | ［66］Paul M B, Vitanyi, Li Ming. Minimum description length induction, bayesianism, and kolmogorov complexity[J]. IEEE Trans. Information theory, 2000,46(2):446～464.
|
| [35] | ［3］Wtukey J. Exploratory data analysis[M]. New York: Addison Wesley, 1977.
|
| [36] | ［5］Bovik A C, Huang T S, Munson D C. Generalization of median filtering using linear combinations of order statistics[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1983,31(6):1342～1350.
|
| [37] | ［7］Geman D, Reynolds G. Constrained restoration and the recovery of discontinuities[J]. IEEE. Trans. Pattern Analysis and Machine Intelligence, 1992,14(3):367～383.
|
| [38] | ［9］You Yuli, Kaveh D. Fourth-order partial differential equations for noise removal[J]. IEEE Trans. Image Processing, 2000,9(10):1723～1730.
|
| [39] | ［12］Deng Liping, Harris J G. Wavelet denoising of chirp-like signals in the Fourier domain[A]. In:Proceedings of the IEEE International Symposium on Circuits and Systems[C]. Orlando USA, 1999:Ⅲ-540-Ⅲ-543.
|
| [40] | ［14］Baraniuk R G. Wavelet soft-thresholding of time-frequency representations[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Texas USA,1994:71～74.
|
| [41] | ［16］Hsung T C, Chan T C L, Lun D P K et al. Embedded singularity detection zerotree wavelet coding[A].In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:274～278.
|
| [42] | ［18］Liu Bin, Wang Yuanyuan, Wang Weiqi. Spectrogram enhancement algorithm: A soft thresholding-based approach[J]. Ultrasound in Medical and Biology, 1999,25(5):839～846.
|
| [43] | ［20］Donoho D L, Johnstone I M. Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika, 1994,81:425～455.
|
| [44] | ［22］Donoho D L, Johnstone I M, Kerkyacharian G et al. Wavelet shrinkage:asymptopia?[J]. Journal of royal statistics society series(B), 1995,57:301～369.
|
| [45] | ［24］Shark L K, Yu C. Denoising by optimal fuzzy thresholding in wavelet domain[J]. Electronics Letters, 2000,36(6):581～582.
|
| [46] | ［26］Weyrich N, Warhola G T. Wavelet shrinkage and generalized cross validation for image denoising[J]. IEEE Trans. Image Processing, 1998,7(1):82～90.
|
| [47] | ［28］Zhang Xiao Ping, Desai M D. Adaptive denoising based on SURE risk[J]. IEEE Signal Processing Letters, 1998,5(10):265～267.
|
| [48] | ［30］Han K J, Tewfik A H. Hybrid wavelet transform filter for image recovery[A].In:Proceedings of the International Conference on Image Processing[C]. Chicago USA, 1998:540～544.
|
| [49] | ［32］Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage[J]. J.ASA, 1995,90:1200～1223.
|
| [50] | ［34］Badulescu P, Zaciu R. Removal of mixed-noise using order statistic filter and wavelet domain Wiener filter[A]. In:Proceedings of the International Semiconductor Conference[C]. Sinaia Romania, 1999:301～304.
|
| [51] | ［36］Malfait M, Roose D. Wavelet-based image denoising using a Markov random field a priori model[J]. IEEE Trans. Imgae Processing, 1997,6(4):549～565.
|
| [52] | ［38］Chang S G, Yu Bin, Vetterli M. Adaptive wavelet thresholding for image denoising and comopression[J]. IEEE Trans. Image Processing, 2000,9(9):1532～1546.
|
| [53] | ［40］Jansen M, Malfait M, Bultheel A. Generalized cross validation for wavelet thresholding[J]. Signal Processing, 1997,56(1):33～44.
|
| [54] | ［41］Hansen M, Yu Bin. Wavelet thresholding via MDL for Natural Images[J]. IEEE Trans. Information Theory, 2000,46(5):1778～1788.
|
| [55] | ［43］Chang S G, Yu Bin, Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denosing[J]. IEEE Trans. Image Processing, 2000,9(9):1522～1530.
|
| [56] | ［44］Krim H, Tucker D, Mallat S G et al. On denoising and best signal representation[J]. IEEE Trans. Information Theory, 1999,5(7):2225～2238.
|
| [57] | ［46］Zhang Xiao Ping, Luo Zhiquan. New time-scale adaptive denoising method based on wavelet shrinkage[A]. In:Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing[C]. Phoenix USA, 1999:1629～1632.
|
| [58] | ［48］Mihcak M K, Kozintsev I, Ramchandran K et al. Low-complexity image denoising based on statistical modeling of wavelet coefficients[J]. IEEE Signal Processing Letters, 1999,6(12):300～303.
|
| [59] | ［50］Demoment G. Image reconstruction and restoration: overview of common estimation structures and problems[J]. IEEE Trans. Acoustics, Speech, and Signal Processing, 1989,37(12):2024～2036.
|
| [60] | ［52］Choi H, Baraniuk R G. Multiple basis wavelet denoising using besov projections[A]. In:Proceedings of International Conference on Image Processing[C]. Kobe Japan,1999:595～599.
|
| [61] | ［54］Sezan M I, Stark H. Tomographic image reconstruction from incomplete view data by convex projection and direct fourier inversion[J]. IEEE Trans. Medical Imaging, 1984,MI-3(2):91～98.
|
| [62] | ［56］Youla D C, Webb H. Image restoration by the method of convex projections. Part I-theory[J]. IEEE Trans. Medical Imaging, 1982,MI-1(2):81～94.
|
| [63] | ［58］John M, Sundaresan S N, Ramakrishna P V. Wavelet based image denoising: VQ-Bayesian technique[J]. Electronics Letter, 1999,35(19):1625～1626.
|
| [64] | ［60］Crouse M S, Baraniuk R G, Nowak R D. Signal estimation using wavelet-Markov models[A]. In:Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing[C]. Munich Germany,1997:3429～3432.
|
| [65] | ［61］Liu Juan, Moulin P. Image denoising based on scale-space mixture modeling of wavelet coefficients[A]. In:Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan,1999:386～390.
|
| [66] | ［63］Ghael S P, Sayeed A M, Baraniuk R G. Improved wavelet denoising via empirical wiener filtering[A]. In:Proceedings of SPIE[C]. San Diego USA, 1997:389～399.
|
| [67] | ［65］Hui Y, Kok C W, Nguyen T Q. Wavelet shrinkage denoising using paraunitary shift-invariant filter banks[A]. In:Proceedings of the IEEE International Symposium on Circuits and Systems[C]. Hong Kong China,1997:185～188.
|
