[1] Berkner K and Wells R O. Smoothness estimates for soft-threshold denoising via translation-invariant wavelet transforms [J].Applied and Computational Harmonic Analysis.2002, 12(1):1- [2] Chang S G, Yu B, and Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denoising [J].IEEE Trans. on Image Proc.2000, 9(9):1522- [3] Lu J, Xu Y S, and Weaver J B, et al.. Noise reduction by constrained reconstructions in the wavelet-transform domain [A]. Proc. IEEE Signal Processing Society Seventh Workshop on Multidimensional Signal Processing[C], Lake Placid, New York, Sept. 23-25, 1991: 1.91.9. [4] Mallat S and Hwang W L. Singularity detection and processing with wavelets [J].IEEE Trans. on Inform. Theory.1992, 38(2):617- [5] Xu Y S, Weaver J B, and Healy D M, et al.. Wavelet transform domain filters: A spatially selective noise filtration technique [J].IEEE Trans. on Image Proc.1994, 3(6):747- [6] Donoho D L. De-noising by soft-thresholding [J].IEEE Trans. on Inform. Theory.1995, 41(3):613- [7] Lang M, Guo H, and Odegard J E, et al.. Noise reduction using an undecimated discrete wavelet transform [J].IEEE Signal Processing Letters.1996, 3(1):10- [8] Hsung T C, Lun DP-K and Siu W-C. Denoising by singularity detection [J].IEEE Trans. on Signal Proc.1999, 47(11):3139- [9] Lu J. Signal recovery and noise reduction with wavelets [D]. Dartmouth College, Hanover, NH, 1993. [10] Lu J and Heally D M. Contrast enhancement of medical images using multiscale edge representation [J].Optical Engineering.1994, 33(7):2151- [11] Rosenfeld A. A nonlinear edge detection technique [A]. Proc. of the IEEE, 1970, 58(5): 814816. [12] Donoho D L and Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage [J].J. of the Amer. Statist. Assoc.1995, 90(432):1200- [13] Jansen M. Noise reduction by wavelet thresholding [M]. Springer Verlag, Lecture notes in Statistics (161), 2001. [14] Pan Q, Zhang L, and Dai G Zh, et al.. Two denoising methods by wavelet transform [J].IEEE Trans. on Signal Proc.1999, 47(12):3401- [15] Zhang L, Bao P, and Pan Q. Threshold analysis in wavelet-based de-noising [J]. IEE Electronics Letters. 2001, 37(24): 14851486. [16] Zhang L and Bao P. Denoising by spatial correlation thresholding [J].IEEE Trans. on Circuits and Systems for Video Technology.2003, 13(6):535- [17] Zhang L and Bao P. Edge detection by scale multiplication in wavelet domain [J].Pattern Recognition Letter.2002, 23(6):1771- [18] Bao P and Zhang L. Noise reduction for magnetic resonance images via adaptive multiscale products thresholding [J].IEEE Trans. on Medical Imaging.2003, 22(9):1089- [19] Zhang L, Bao P, and Wu X L. Hybrid inter-and intra-wavelet scale image restoration [J].Pattern Recognition.2003, 36(8):1737- [20] 潘泉,张磊,张洪才等. 子波域自适应滤波算法[J]. 航空学报, 1997, 18(5): 583586. [21] 潘泉,戴冠中,张洪才等. 基于阈值决策的子波域去噪方法[J]. 电子学报, 1998, 26(1): 115117. Pan Quan, Dai Guan-zhong, and Zhang Hong-cai, et al.. A threshold selection method for hard-threshold filter algorithm. Acta Electronica Sinica, 1998, 26(1): 115117. [22] 张磊,潘泉. 一种子波域滤波算法的改进[J]. 电子学报, 1999, 27(2): 1921. Zhang Lei and Pan Quan. Improvements on an adaptive filtering algorithm in wavelet transform domain. Acta Electronica Sinica, 1999, 27(2): 1921. [23] 王博,潘泉,张洪才. 基于子波分解的信号滤波算法[J]. 电子学报, 1999, 27(11), 7174. Wang Bo, Pan Quan, and Zhang Hong-cai. Signal filtering algorithm based on the wavelet transformation. Acta Electronica Sinica, 1999, 27(11), 7174. [24] 张磊,潘泉,张洪才等. 小波域滤波阈值参数c的选取[J]. 电子学报,2001, 29(3): 400402. Zhang Lei, Pan Quan, and Zhang Hong-cai, et al.. On the determination of threshold in threshold-based de-noising by wavelet transform. Acta Electronica Sinica, 2001, 29(3): 400402. [25] Rioul O and Vetterli M. Wavelets and signal processing [J]. IEEE Signal Processing Magazine, 1991, 8(4): 1438. [26] Mallat S. A theory of multiresolution signal decomposition: The wavelet transform [J].IEEE Trans. on Pattern Anal. and Machine Intel.1989, 11(7):674- [27] Vidakovic B L and Ozoya C B. On time-dependent wavelet denoising [J].IEEE Trans. on Signal Proc.1998, 46(9):2549- [28] Carl Taswell. The what, how and why of wavelet shrinkage denoising [J].Computing in Science and Engineering.2000, 2(3):12- [29] 赵瑞珍. 小波理论及其在图像、信号处理中的算法研究[D]. [博士论文], 西安:西安电子科技大学, 2001. [30] Dragotti P L and Vetterli M. Wavelet footprints: theory, algorithms, and applications [J]. J. Amer. Statist. Assoc., 2003, 51(5): 13061323. [31] Johnstone I M and Silverman B W. Wavelet threshold estimators for data with correlated noise [J].J. Royal Statistical Society B.1997, 59(2):319- [32] Jansen M and Bultheel A. Multiple wavelet threshold estimation by generalized cross validation for data with correlated noise [J].IEEE Trans. on Image Proc.1999, 8(7):947- [33] Badulescu P and Zaciu R. Removal of mixed-noise using order statistic filter and wavelet domain Wiener filter [A]. Proceedings of the International Semiconductor Conference[C]. Sinaia Romania, 1999: 301304. [34] Coifman R R and Donoho D L. Translation-invariant de-noising [A]. Wavelets in Statistics of Lecture Notes in statistics 103[C]. New York: Springer-Verlag, 1994: 125150. [35] Arne Kovac. Wavelet thresholding for unequally spaced data [D], Ph.D. Thesis, Faculty of Science, University of Bristol, 1998. [36] Vanraes E, Jansen M, and Bultheel A. Stabilized wavelet transforms for non-equispaced data smoothing [J].Signal Processing.2002, 82(12):1979- [37] Malfait M and Roose D. Wavelet-based image denoising using a Markov random field a priori model [J].IEEE Trans.on Imgae Proc.1997, 6(4):549- [38] Shark L K and Yu C. Denoising by optimal fuzzy thresholding in wavelet domain [J].Electronics Letters.2000, 36(6):581- [39] Mihcak M, Kozintsev I, and Ramchandran K, et al.. Low- complexity image denoising based on statistical modeling of wavelet coefficients [J].IEEE Signal Processing Lett.1999, 6(12):300- [40] Pizurica A and Philips W. Estimating probability of presence of a signal of interest in multiresolution single- and multiband image denoising [J]. IEEE Trans. on Image Proc. (in press). [41] Mallat S. A theory for multiresolution signal decomposition: The wavelet representation [J].IEEE Trans. on Pattern Anal. and Machine Intel.1989, 11(7):674- [42] Liu Juan. Wavelet-based statistical modeling and image estimation [D]. Ph.D. Thesis, Dept. Electrical Engineering, University of Illinois at Urbana- Champaign, 2001. [43] Hall P, Kerkyacharian G, and Picard D. On the minimax optimality of block thresholded wavelet estimators [J]. Statistica Sinica, 1999, 9(1): 3350. [44] Moulin P, and Liu J. Analysis of multiresolution image denoising schemes using generalized-Gaussian and complexity priors [J].IEEE Trans. on Inform. Theory.1999, 45(3):909- [45] Hansen M and Yu B. Wavelet thresholding via MDL for natural images [J].IEEE Trans. on Inform. Theory.2000, 46(5):1778- [46] Slader B M, and Swami A. Analysis of multiscale products for step detection and estimation [J].IEEE Trans. on Inform. Theory.1999, 45(4):1043- [47] Sender L and Selesnick I W. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J].IEEE Trans. on Signal Proc.2002, 50(11):2744- [48] Shapiro J M. Embedded image coding using zerostrees of wavelet coefficients [J].IEEE Trans. on Signal Proc.1993, 41(12):3445- [49] Banham M R and Katsaggelos A K. Spatially adaptive wavelet-based multiscale image restoration [J].IEEE Trans.on Image Proc.1996, 5(4):619- [50] Crouse M S, Nowak R D, and Baraniuk R G. Wavelet-based statistical signal processing using hidden Markov models [J]. IEEE Trans. on Signal Proc., 1998, 4(46): 886902. [51] Fan G and Xia X G. Improved hidden Markov models in the wavelet-domain [J].IEEE Trans. on Signal Proc.2001, 49(1):115- [52] Romberg J K, Choi H, and Baraniuk R G. Bayesian tree-structured image modeling using wavelet-domain hidden Markov models [J].IEEE Trans. on Image Proc.2001, 10(7):1056- [53] Pizurica A, Philips W, and Lemahieu I, et al.. A versatile wavelet domain noise filtration technique for medical imaging [J].IEEE Trans. on Medical Imaging.2003, 22(5):323- [54] Liu Juan and Moulin P. Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients [J].IEEE Trans. on Image Proc.2001, 10(11):1647- [55] Portilla J, Strela V, and Wainwright M J, et al.. Adaptive wiener denoising using a Gaussian scale mixture model in the wavelet domain. Proceedings of the Eight International Conference on Images Processing. Thessaloniki, Greece, 2001, 2: 3740. [56] Bruce A G and Gao H-Y. Understanding waveShrink: variance and bias estimation [J].Biometrika.1996, 83(4):727- [57] Bruce A G and Gao H-Y. Waveshrink with firm shrinkage [J]. Statistica Sinica, 1997, 7(4): 855874. [58] Gao H-Y. Wavelet shrinkage denoising using the non- negative garrote [J].J. of Computational and Graphical Statistics.1998, 7(4):469- [59] Zhang X-P and Desai M D. Adaptive denoising based on SURE risk [J].IEEE Signal Processing Lett.1998, 5(10):265- [60] Abramovich F, Sapatinas T, and Silverman B W. Wavelet thresholding via a Bayesian approach [J].J. Royal Statistical Society B.1998, 60(3):725- [61] Vidakovic B. Nonlinear wavelet shrinkage with Bayes rules and Bayes factor [J].J. of the Amer. Statist. Assoc.1998, 93(5):173- [62] Nason G P. Wavelet shrinkage using cross-validation [J]. J. Royal Statistical Society B, 1996, 58(2): 463479. [63] Jansen M, Malfait M, and Bultheel A. Generalized cross validation for wavelet thresholding [J].Signal Processing.1997, 56(1):33- [64] Abramovich F and Benjamini Y. Thresholding of wavelet coefficients as multiple hypotheses testing Procedure [Z]. In A. Antoniadis and G. Oppenheim, editors, Wavelets and Statistics, Springer, New York, 1995: 614. [65] Chang S G, Yu B, and Vetterli M. Adaptive wavelet thresholding for image denoising and compression [J].IEEE Trans. on Image Proc.2000, 9(9):1532- [66] Cohen I, Raz S, and Malah D. Translation-invariant denoising using the minimum description length criterion [J].Signal Processing.1999, 75(3):201- [67] Downie T R, and Silverman B W. The discrete multiple wavelet transform and thresholding methods [J].IEEE Trans.on Signal Proc.1998, 46(9):2558- [68] Bui T D and Chen G. Translation-invariant denoising using multiwavelets [J].IEEE Trans. on Signal Proc.1998, 46(12):3414- [69] Felix C A Fernandes. Directional.[J].shift-insensitive, complex wavelet transforms with controllable redundancy [D]. Texas AM University, Ph.D. Thesis, Houston.2002,:- [70] Candes E. Ridgelets: theory and applications [D]. Ph.D. Thesis, Department of Statistics, Stanford University, 1998. [71] Starck J L, Candes E J, and Donoho D L. The Curvelet transform for image denoising [J].IEEE Trans. on Image Proc.2002, 11(6):670- [72] Krim H, Tucker D, and Mallat S, et al.. On denoising and best signal representation [J]. IEEE Trans. on Inform. Theory, 1999, 5(7): 22252238. [73] Claypoole R L, Baraniukm R G, and Nowark R D. Adaptive wavelet transforms via lifting [A]. Proc. IEEE Conf. on Acoustics, Speech and Signal Proc.[C], Phoenix, May 12-15, 1999, vol.3: 15131516. [74] Angelini E, Esser Y J P, and Van Heertum R, et al.. Fusion of brushlet and wavelet denoising methods for nuclear images [A]. IEEE International Symposium on Biomedical Imaging [C], Macro to Nano, April 15-18, 2004, 2: 11871191. [75] Do M N and Vetterli M. Contourlets [M]. New York, Academic Press, 2003.
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