Application Of PM Model Based On Dynamical Threshold In Image Denoising

In this paper,we propose a new dynamical threshold based Perona-Malik(DTPM)model for image denoising.As one of the most famous anisotropic diffusion equations,the PM model has been widely used in noise removal,image segmentation,edge detection and image enhancement.However,the major disadvantages of the traditional PM model are the high difficulty of choosing the best threshold and the tendency of impairing details,so that the denoising is either excessive or insufficient in the whole process.These defects are more obviously when dealing with the image that polluted by noise from multiple distributions.By designing a dynamical threshold function in the edge indicator,we establish a new PM model that can change the diffusion mode and strength adaptively according to the image features.In addition,we also prove the existence and uniqueness of the weak solution of the model,give the numerical iterative scheme of the model,and prove the convergence of the iterative scheme.After theoretical analysis,we give an experimental approach to show the efficiency of this kind of model,especially to the image containing multi-noise and multi-details.