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一、简介

1 用途

二、源代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%I=imread('toys.bmp'); % load imageI=double(I(20:120,10:105)); % cut a piece, convert to double%%% Parametersstd_n=10; var_n=std_n^2;  % Gaussian noise standard deviationreduced_pw = 1.5*var_n;   % power to reduce in first phasesig_w = 5; ws=4*sig_w+1;  % window size%%%%%%%%%%%%%%%%% Add noiseIn = randn(size(I))*std_n;I0 = I + In;  % noisy input image% show original and noisy imagesfigure(1); imshow(uint8(I)); title('Original')figure(2); imshow(uint8(I0)); title('Noisy image')snr_noisy=db(I,I0)% run normal tv - strong denoisingtic;J=I0; ep_J=0.1; % minimum mean change in image JJ_old=0;lam=0; iter=10; dt=0.2; ep=1; while (mean(mean(abs(J - J_old))) > ep_J),  % iterate until convergence   J_old=J;   J=tv(J,iter,dt,ep,lam,I0); % scalar lam   lam = calc_lam(J,I0,reduced_pw); % update lambda (fidelity term)end % while% figure(3); imshow(uint8(J)); title('residue TV')% snr_residue= db(I,J)Ir=I0-J;  % Ir scalarPr = mean(mean(Ir.^2)); % power of residueLV = loc_var(Ir,ws,sig_w^2);  % local variance (local  power of the residue )% P=mean(mean(LV));Pxy=1*(var_n^2)./LV;  %%  Sxy    inverse proportional to the LV%%% Varying Lambdalamxy=zeros(size(I0));J=I0; J_old=0; ep_J=0.001;%eps=0.01;while (mean(mean(abs(J - J_old))) > ep_J),  % iterate until convergence   J_old=J;   J=tv(J,iter,dt,ep,lamxy,I0); % adaptive lam   %J=tv(J,iter,dt,ep_J,lamxy,I0);   lamxy = calc_lamxy(J,I0,Pxy,sig_w); % update lambda (fidelity term)end % whilefigure(3); imshow(uint8(J)); title('Adaptive TV')snr_adap= db(I,J)toct1=toc%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Run scalar TV denoising for comparision %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ticJ=I0; % paramsep_J = 0.01; % minimum mean change in image Jlam=0; J_old=0;i=0;while (mean(mean(abs(J - J_old))) > ep_J),  % iterate until convergence   J_old = J;   J=tv(J,iter,dt,ep,lam,I0);     % scalar lam   lam = calc_lam(J,I0,var_n,ep); % update lambda (fidelity term) end % for i% Ir=I0-J;  % Ir scalar% Pr = mean(mean(Ir.^2)); % power of residuefunction Ig=gauss(I,ks,sigma2)%private function: gauss (by Guy Gilboa):% Ig=gauss(I,ks,sigma2)% ks - kernel size (odd number)% sigma2 - variance of Gaussian[Ny,Nx]=size(I);hks=(ks-1)/2;  % half kernel sizeif (Ny
   
    1)      xL=mean(I(:,1:hks)')'; xR=mean(I(:,Nx-hks+1:Nx)')';% xL,xR :Ny x 1   else      xL=I(:,1); xR=I(:,Nx);   end   eI=[xL*ones(1,hks) I xR*ones(1,hks)];   % Ny x  Nx+2hks   if (hks>1)      xU=mean(eI(1:hks,:)); xD=mean(eI(Ny-hks+1:Ny,:));  % xU,xD: 1 x Nx+2hks   else   	xU=eI(1,:); xD=eI(Ny,:);      end
   

三、运行结果

四、备注

完整代码或者代写添加QQ1575304183

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