Improved Model Of Anisotropic Diffusion Image Denosing

Image denoising is a very important part of the image processing, the purpose ofimage denoising is reducing the noise of the image as much as possible, at the sametime, the original image information as much as possible. The edges is easily lost in theprocess of noise smoothing by traditional image denoising methods such as gaussianfiltering, median filtering, average filtering, etc.. Anisotropic diffusion denoisingmethod based on PDE provides a new way to solve this contradiction.Based on the analysis of behavior of the diffusion coefficient in anisotropicdiffusion equation, the traditional diffusion coefficient is improved in the model.Because edge points can not be distinguished only by gradient, the improved model inthe introduction of the edge sharping factor of second order partial deviation into thediffusion coefficient, In addition, the improved model adapt different diffusion behaviorat different gradient levels. Thus it can not only effectively protect the edge, but also toavoid the distortion of small scale sensitive to noise and details. The improveddiffusion coefficient is applied to the anisotropic diffusion C model and anisotropicdiffusion tensor Wieckert model in this paper, then the improved C model and Wieckertmodel are obtained. At last, the correlation coefficient is used to judge the denoisingImage quality, the experimental results show that the improved model is superior totraditional model in denoising effect as well as denoising time.