【算法随记六】一段Matlab版本的Total Variation(TV)去噪算法的C语言翻译。
最近看到一篇文章讲IMAGE DECOMPOSITION，里面提到了将图像分为Texture layer和Structure layer，测试了很多方法，对于那些具有非常强烈纹理的图像，总觉得用TV去燥的方法分离的结果都比其他的方法都要好（比如导向、双边），比如下图：
再比如：
可见TV可以把纹理很好的提取出来。
function dualROF() clc f0=imread('rr.bmp'); f0=f0(:,:,1); [m,n]=size(f0); f0=double(f0); lamda=30; % smoothness paramter, the larger the smoother tao=.125; % fixed do not change it. p1=zeros(m,n); p2=zeros(m,n); tic for step=1:100 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% div_p=div(p1,p2); cx=Fx(div_p-f0/lamda); cy=Fy(div_p-f0/lamda); abs_c=sqrt(cx.^2+cy.^2);
p1=(p1+tao*cx)./(1+tao*abs_c);
p2=(p2+tao*cy)./(1+tao*abs_c);
end u=f0-lamda*div_p; toc figure; imagesc(f0); colormap(gray); axis off; axis equal; figure; imagesc(u); colormap(gray); axis off; axis equal; % Compute divergence using backward derivative function f = div(a,b) f = Bx(a)+By(b); % Forward derivative operator on x with boundary condition u(:,:,1)=u(:,:,1) function Fxu = Fx(u) [m,n] = size(u); Fxu = circshift(u,[0 -1])-u; Fxu(:,n) = zeros(m,1); % Forward derivative operator on y with boundary condition u(1,:,:)=u(m,:,:) function Fyu = Fy(u) [m,n] = size(u); Fyu = circshift(u,[-1 0])-u; Fyu(m,:) = zeros(1,n); % Backward derivative operator on x with boundary condition Bxu(:,1)=u(:,1) function Bxu = Bx(u) [~,n] = size(u); Bxu = u - circshift(u,[0 1]); Bxu(:,1) = u(:,1); Bxu(:,n) = -u(:,n-1); % Backward derivative operator on y with boundary condition Bxu(1,:)=u(1,:) function Byu = By(u) [m,~] = size(u); Byu = u - circshift(u,[1 0]); Byu(1,:) = u(1,:); Byu(m,:) = -u(m-1,:);