Remote Sensing Image De-noising Method Based On Partial Differential Equation

The quality of remote sensing images is always degraded by noises during acquisition and transmission,which make it difficult to identify and analysis image.So it is much necessary to effectively remove these noise before using it. In recent years,nonlinear diffusion model based image de-noising have received considerable attention in the field of remote sensing image application since the model can well preserve the edge,and the method of de-noising have become a new image processing tools after wavelet.First,the paper introduce the development of partial differential equation de-noising,and their de-noising characteristics and focus on introducing the P-M equation.Second,the paper introduce the theoretical background of diffusion and variation de-noising.Finaly,the paper proposes three de-noising models based on PDE as follows:(1)This paper proposes a PDE-based hybrid model to de-noise the pre-processed remote sensing images, which are often polluted by Gaussian and salt-pepper noises. Our model solves the excessive diffusion problem at smooth regions and staircase effect presented in traditional pure anisotropic diffusion model. Meanwhile, the proposed model overcomes the shortage of 4-order PDE model that tends to lose much edge information.(2)The paper proposes a new nonlinear diffusion model by introducing wavelet modulus maximum into diffusion model and gives a discrete scheme.Our model solve the noise not easy to remove near the strong edge presented in P-M .Meanwhile,the proposed model overcome the shortage of ALM model that tend to bluring and losing singular point. Our model can not only efficiently remove noise in remote sensing image,but also simultaneously retain detail information, such as edge and texture.Experimental results illustrate the effectiveness and stability of the proposed model.(3)This paper first analyzes fundamental principle and deficiency of the original total variation model (TV model) and its improved model (M-model). Then an improved total variation model is proposed based on standard gradient and edge guidance function. Our model solves the staircase effect presented in tradional TV model,and the shortage that M-model tends to the excessive smooth and losing texture and detail information .