遥感图像的噪声分析、评估和滤波一直是遥感图像处理的一个重要研究领域。近年来，基于非线性扩散模型的图像去噪方法因其在对图像进行去噪的同时，对图像的特征信息具有一定的保护作用而受到遥感图像应用领域的关注并成为研究热点。针对P-M方程和ALM模型在去除遥感高斯噪声时所存在的对图像强边缘附近的噪声难以去除和可能造成奇异点的模糊或丢失等问题，将小波变换模极大值进入到扩散模型中提出一种新的非线性扩散模型，并给出模型的离散化算法。该模型有效地克服了P-M模型和ALM模型在图像去噪过程中的不足，在有效去除噪声的同时，很好地保留了遥感图像的边缘和纹理细节信息。实验结果验证了所提出模型的有效性和稳定性。

The noise analysis evaluation and filtering of remote sensing image have always been an important research field.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 efficiently remove noises in remote sensed images,while well preserving the texture information.The paper proposes a new nonlinear diffusion model by introducing wavelet modulus maximum into the diffusion model and gives a discrete scheme.Our model removes the noise better than P-M model. Meanwhile,the proposed model overcomes the shortage of ALM model that tend to bluring and losing singular point. Our model can not only efficiently remove noise in remote sensing images,but also simultaneously retain detail information, such as edge and texture.Experimental results illustrate the effectiveness and stability of the proposed model.