图像去噪基于matlab全变分算法图像去噪【含Matlab源码 1324期】.zip

function u = SB_ITV(g,mu) % Split Bregman Isotropic Total Variation Denoising % % u = arg min_u 1/2||u-g||_2^2 + mu*ITV(u) % % Refs: % *Goldstein and Osher, The split Bregman method for L1 regularized problems % SIAM Journal on Imaging Sciences 2(2) 2009 % *Micchelli et al, Proximity algorithms for image models: denoising % Inverse Problems 27(4) 2011 % % Benjamin Trémoulhéac % University College London % b.tremoulheac@cs.ucl.ac.uk % April 2012 g = g(:); n = length(g); [B Bt BtB] = DiffOper(sqrt(n)); b = zeros(2*n,1); d = b; u = g; err = 1;k = 1; tol = 1e-3; lambda = 1; while err > tol fprintf('it. %g ',k); up = u; [u,~] = cgs(speye(n)+BtB, g-lambda*Bt*(b-d),1e-5,100); Bub = B*u+b; s = sqrt(Bub(1:n).^2 + Bub(n+1:end).^2); d = [max(s-mu/lambda,0).*Bub(1:n)./s ; max(s-mu/lambda,0).*Bub(n+1:end)./s ]; b = Bub-d; err = norm(up-u)/norm(u); fprintf('err=%g \n',err); k = k+1; end fprintf('Stopped because norm(up-u)/norm(u) <= tol=%.1e\n',tol); end function [B Bt BtB] = DiffOper(N) D = spdiags([-ones(N,1) ones(N,1)], [0 1], N,N+1); D(:,1) = []; D(1,1) = 0; B = [ kron(speye(N),D) ; kron(D,speye(N)) ]; Bt = B'; BtB = Bt*B; end