A study of blind denoising algorithms for two-scale real images based on partial differential equations

Abstract

In order to better preserve the details and texture information in the image, a dual scale real image blind denoising algorithm based on partial differential equations is studied. Input real image information, perform small-scale denoising based on differential curvature partial differential equations, and combine with a new diffusion coefficient function to distinguish the edges, flat areas, and noise of the image, allowing the algorithm to retain more subtle information such as weak edges and textures while removing noise. For the large-scale information in the input real image, a multi-stage partial differential equation is used for denoising processing. Based on the mixed denoising method of 8-neighborhood and implicit curvature, a weight function is constructed to control the proportion of the two types of equations in image denoising, effectively achieving the goal of large-scale image denoising. The experimental results show that in the MATLAB coding platform, this algorithm can eliminate different types of noise, preserve more edge and detail information of the image, and improve the registration and recognition accuracy in the image application process.