［2］Huang Y, Van Genderen J L. SAR speckle reduction: a reviewof filtering techniques and an evaluation of filter performance [R]. Technical report, International Institute for Aerospace andEarth Sciences(ITC), Enschede, The Netherlands, 1996.
[2]
［4］.Mallat S. Multifrequency channel decompositions of images andwavelets models [J]. IEEE Trans. Acoust., Speech, Signal Processing, 1989,37(12) :2091～2110.
[3]
［6］Donoho D L. De-Noising by soft-thresholding[J]. IEEE Trans.Information theory, 1995,41 (3): 613～627.
[4]
［8］Dong Y, Froster B C, Milne A K et al. Morgan, Specklesuppression using recursive wavelet transforms [J]. Int. J. Remote sensing, 1998,19(21): 317～330.
[5]
［10］Nason G P, Silverman B W. The stationary wavelet transformand some statistical applications [A]. In: Antoniadis and G. Oppenheim, Eds. Wavelets and Statistics [M], New York: Spring-Verlag, 1995 : 281～299.
[6]
［1］Dewaele P, Wambacq P, Oosterlinck A et al. Comparison of some speckle reduction techniques for SAR images [A]. In: Proceedings of International Geoscience and Remote Sensing Symposium, IGARSS90[C]. New York, 1990:2417～2412.
[7]
［3］Mallat S. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Trans. Pattern Anal. Machine Intell. , 1989,11(7) :674～693.
[8]
［5］Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994,81(3) :425～455.
[9]
［7］Odegard J E, Guo H, Lang M et al. Wavelet based SAR speckle reduction and image compression [J]. SPIE Proc. 1995,2487:259～271.
[10]
［9］Shensha M J. The discrete wavelet transform: Wedding the a trous and mallat algorithms[J]. IEEE Trans. Signal Processing,1992,40(10): 2464～2482.
