Study On Image Denoising Method Based On The Structure Information Of Nonlinear Diffusion Filtering

Digital images have become an indispensable source of information era as an important means of information transmission. However, during the generation, transmission and reception of an image, it is inevitably affected by noise. The blurring, distortion, and obvious noise points would affect the quality of the images, hind the subsequent image analysis and processing. Therefore, improving the image quality, removing noise and maintaining the inherent characteristics have become the most basic and necessary requirements with important theoretical and practical significance.In recent years, due to the application of image processing based on partial differential equations(PDE) was used more widely, the theoretical aspects were also developed a lot. This paper discussed the use of partial differential equations theory for denoising, at the same time, focused on how to more accurately and efficiently reduce image noise and improve image quality to meet the needs of practical application for subsequent processing. The diffusion term in PDE among the diffusion theory is a crucial part, which controls the magnitude and speed of diffusion, is the core of the research subject. Gradient fidelity term is to determine the average greyscale before and after the image processing is stable, and the fake boundary will not be showed. It can be combined with the existing diffusion image denoising, image pre-processing of the completion of denoising step. The main work includes the following aspects:(1)The image denoising method based on the structure information of nonlinear diffusion filtering are analyzed intensively. A new denoising model could fit more angles and boundaries is proposed, and study and optimization of the performance of denoising model. Comparing with the original method show that the model can be retained to obtain higher quality images for subsequent processing.(2) A method for determining an adaptive gradient fidelity term by structural information was proposed, combined with the existing denoising diffusion model. More detailed information and the border can be protected by the model, and obtain higher quality results.