Research On Image Denoising Based On Total Variation Model

As the major medium for storing,obtaining and transmitting information,image are disturbed by external factors inevitably and caused distortion to varying degrees,which severely affect the quality of image and the follow-up image processing.In fact,noise is an important factor in many uncertainties that cause image distortion.Therefore,image denoising has become an important task in image preprocessing in order to ensure the high quality of image in the process of its storage,acquisition and transmission,and the follow-up image processing finished successfully,.Image denoising is widely concerned by many scholars nowadays.The purpose of it is to recover the original noiseless images from those noisy ones to the most extent.However,the traditional image denoising methods,such as mean filtering method,median filtering method and wiener filtering method,which can not get balance between removing noise and preserving image details.In recent years,some mathematical tools have received scholars' attentions because of their good denoising performance on remove noise,such as the partial differential equation theory and variation method.This research of image denoising is based on Total Variation(TV)model.This thesis aims at the problem of TV model only considers the image gradient and staircase effect,based on the TV model,we propose an improved method with weighting function.In this thesis,the improved method not only takes into account the gradient information of the image,but also takes into account the gray-level variance information of the image.Firstly,considering the difference between gradient and gray-level variance values and the influence of noise,the gradient values and gray-level variance values of each pixel in the image are preprocessed.Secondly,to control the diffusion intensity,the proposed weighting function in this thesis is introduced in the regularization term of TV model.Finally,we use an iterative algorithm to denoise the image.The experiments show that the proposed model can effectively reduce the staircase effect and has better denoising effect.