The noise analysis and elimination in remote sensing images has attracted considerable attention

and has become an important research field for remote sensing image processing. In this paper

we propose a wavelet threshold method to de-noise the Gaussian noise in remote sensing image to make the edge fuzzy causing by over the existence of the "strangulation" of the wavelet coefficients

as well as P-M model usually tends to make the image gray sub-constant

resulting the so-called "massive" effect problem

This paper proposes a new remote sensing image denoising model based on wavelet partial differential equations (PDE) to address the above mentioned issue.This model decomposes remote sensing images by wavelets and maintain the low-frequency subband information.Only with noise and the edge's high-frequency sub-band based on sub-band directional characteristics of the nonlinear anisotropic diffusion

this model can remove Gaussian noise well and

at the same time

can also protect the edge features and details of remote sensing image

and avoids to appear the piecewise constant phenomenon.Experimental results show that our model gains 1~2dB higher PSNR than the class of zero-tree based on Bayes model threshold and P-M model.