Image Denosing Method Based On L_p Norm

In the process of image acquisition and transmission,inevitably introduce noise led to the decrease of the quality of the image,and then result in the degradation of quality,which will seriously affect the subsequent image processing.Therefore,image denoising is an important part of image processing.In recent years,with the rise of compressive sensing and sparse representation theory,low rank sparse matrix recovery theory has been paid more and more attention by researchers.Therefore,image denoising method based on low rank matrix recovery is still a hot topic.Based on the in-depth study of low rank and sparse theory,this paper establishes an image denoising model based on Schatten-p norm and a hybrid noise removal model based on l_p norm regularization.The main work of this paper is as follows:1.A minimization model based on Schatten-p norm is proposed to solve the denoising problem of image gaussian noise.Using nonlocal self-similarity of images formed a similar image block matrix,established a minimization model based on Schatten-p norm,and then introducing minimization optimization technology and nuclear norm minimization model to solve the proposed minimization model based on the Schatten-p norm,Using Schatten-p norm to reduce the rank function,is closer to the rank function than the convex one of the rank function(the kernel norm of the matrix).2.Proposed a hybrid noise removal model based on l_p norm regularization.First,constructs a low rank-sparse model based on l_p norm,processes both additive white gaussian noise and impulse noise at the same time,then alternating direction multiplier method is used to solve the optimization model,and then applied it to the observed image mixed noise removal.A large number of experimental results show that the proposed method can effectively remove the gaussian noise and the mixed noise and restore the original image compared with the present advanced hybrid noise removal method.