Improved Image Denoising Algorithm Based On Partial Differential Equation

At present, image de-noising technology is widely used in the fields of computer science, engineering, medicine and so on, which has high study value and application prospects. In the process of image de-noising, how to effectively remove the image noise while protecting the image structure features, has become an urgent problem to be solved. Image de-noising algorithm based on partial differential equations can be used to smooth the noise and image, which can solve this problem well, so as to become the focus of the study of image de-noising algorithm. In this paper, we first introduce the typical image de-noising methods based on PDE, then carry on the in-depth study and broaden the study scope of this kind of algorithm, the final simulation is made by MATLAB software, and the simulation results are objective and subjective evaluation view. As following,1?This paper analyzes the theory of anisotropic diffusion, considering the fact that expressing local characters only based gradient information is not enough to determine diffusion extent. So we put forward:(1)Image de-noising model based on the improved Demons algorithm; (2) Anisotropic filtering model of high-fidelity based on threshold optimization; (3)Diffusion denoising model based on the wavelet and biharmonic equation. All the three models combine high order derivative with gradient to control the whole diffusion de-noising extent, which makes up the shortcoming of blurry image structure of traditional diffusion model. The experiment results show that novel model can remove noise and protect image structure character comparing with traditional de-noising model.2?This paper analyzes the total variation de-noising model, develops its fidelity term:(1) Coupling image de-noising model based on total variation, this model builds a trend fidelity term which inhibit 'staircase effect'; (2)Wavelet transform image denoising method based on curvature variation, this model uses level set of enhanced image to build a curvature driving function which is based on level set, then introduces curvature driving function as a correcting factor into variation model, I build a curvature variation model, which controls image entire structure. Experiment results indicate that the two developed models have obvious de-noising performance, good visual effect and overall performance.3?The simulation experiment with MATLAB software is carried out to verify the superiority of the improved algorithm. The final simulation results are consistent with the conclusions of theoretical analysis, which shows that the improved algorithm has better comprehensive performance than the traditional algorithm, and has better practical application value. In the end, all the work of this paper is summarized, and the future development of this field is also discussed.