图像的噪声过滤和增强是数字图像处理中非常重要的组成部分.在图像处理过程中,为了既有效地去除噪声,又能够较好地保持图像的边缘和重要的细节信息,在Perona-Malik各向异性扩散模型（PM模型）的基础上,通过对变分方法的扩散方程中扩散系数的改进,提出了一个对噪声图像更有效更具有适应性的去噪扩散模型.该模型针对不同的梯度大小采用了不同的扩散系数.在实际处理过程中该模型不仅能够有效地保持图像的边缘,而且还能够克服PM模型对小尺度噪声敏感和部分边缘和细节失真的问题.实验结果表明,改进的扩散模型的性能优于PM模型,是一种较为理想的保边缘平滑模型.

Image filtering and enhancement play a very important role in digital image processing.The anisotropic diffusion is a selective smoothing technique that effectively employs intra-region smoothing without limitation and inhibits inter-region smoothing.In order to remove noise effectively and preserve edges and key details,a more effective and adaptive diffusion algorithm is to be proposed in this paper.We build a new diffusion coefficient in partial derivative equation(PDE) with the advantages of two existing diffusion coefficients and incorporate the proposed scheme with the nonlinear time-dependent cooling technique for gradient threshold.The proposed algorithm is based upon a selective and improved diffusion coefficient and performs adaptively towards different gradients.The improved algorithm not only preserves image edges,but also smoothes small scale features and avoids distortion of image edges and details. It has been shown from the experiments that the improved scheme has superiority capability over the Perona-Malik scheme and it is a robust anisotropic diffusion.