Generalized Variational Denoising Model Based on Smooth Kernel
Yiyan Wang · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2010
Variarional model based on PDE is one of the best schemes for image processing.To improve the staircase effect of conventional total variational model in solving the inverse problem of image denoising,a generalized variational denoising model based on smooth kernel(u^=argminu{J(u)=∫Ω(|u1|)dxdy+λ2∫Ω|u-u012dxdy})is proposed.This model uses a smooth kernel function of general form as the regularized term of image,an edge preserving potential function was adopted,which had good bobustness to noises.Then the partial differential equation of the proposed model is deduced by variation approach.Finally a weighted gradient descent flow is developed for image denoising with an iterative algorithm based on semi-point scheme,that is un+1i,j=uni,j+δt·[(′(|u1)1|u1|uξξ+″(1|u1|)uηη)ni,j+(λ(u0-u))ni,j].Experimental results show that the proposed model has good performance in image denoising.It can suppress Gaussian noise very effectively and preserve image details very well,meanwhile,the restored images that are obtained by the proposed model have better objective quality(PSNR)and subjective vision effect than that by the conventional total variational model.