Serendipity
3ca079a980
- 中文目录名改为英文：cDNA图像处理实例→examples，参考资料→references - web→app（Flask应用标准命名） - 输出目录平移到 data/output/simple/ 和 data/output/full/ - 输入图像统一到 data/input/ - 构建脚本移到 scripts/ - 源码文件改用 snake_case 命名 - 更新所有文件路径引用和文档
86 lines
2.5 KiB
Matlab
86 lines
2.5 KiB
Matlab
function u = tvdenoise(f,lambda,Tol)
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%TVDENOISE Total variation grayscale and color image denoising
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% u = TVDENOISE(f,lambda) denoises the input image f. The smaller
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% the parameter lambda, the stronger the denoising.
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%
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% The output u approximately minimizes the Rudin-Osher-Fatemi (ROF)
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% denoising model
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%
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% Min TV(u) + lambda/2 || f - u ||^2_2,
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% u
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%
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% where TV(u) is the total variation of u. If f is a color image (or any
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% array where size(f,3) > 1), the vectorial TV model is used,
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%
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% Min VTV(u) + lambda/2 || f - u ||^2_2.
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% u
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%
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% TVDENOISE(...,Tol) specifies the stopping tolerance (default 1e-2).
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%
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% The minimization is solved using Chambolle's method,
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% A. Chambolle, "An Algorithm for Total Variation Minimization and
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% Applications," J. Math. Imaging and Vision 20 (1-2): 89-97, 2004.
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% When f is a color image, the minimization is solved by a generalization
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% of Chambolle's method,
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% X. Bresson and T.F. Chan, "Fast Minimization of the Vectorial Total
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% Variation Norm and Applications to Color Image Processing", UCLA CAM
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% Report 07-25.
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%
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% Example:
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% f = double(imread('barbara-color.png'))/255;
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% f = f + randn(size(f))*16/255;
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% u = tvdenoise(f,12);
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% subplot(1,2,1); imshow(f); title Input
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% subplot(1,2,2); imshow(u); title Denoised
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% Pascal Getreuer 2007-2008
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if nargin < 3
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Tol = 1e-2;
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end
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if lambda < 0
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error('Parameter lambda must be nonnegative.');
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end
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dt = 0.25;
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N = size(f);
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id = [2:N(1),N(1)];
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iu = [1,1:N(1)-1];
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ir = [2:N(2),N(2)];
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il = [1,1:N(2)-1];
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p1 = zeros(size(f));
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p2 = zeros(size(f));
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divp = zeros(size(f));
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lastdivp = ones(size(f));
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if length(N) == 2 % TV denoising
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while norm(divp(:) - lastdivp(:),inf) > Tol
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lastdivp = divp;
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z = divp - f*lambda;
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z1 = z(:,ir) - z;
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z2 = z(id,:) - z;
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denom = 1 + dt*sqrt(z1.^2 + z2.^2);
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p1 = (p1 + dt*z1)./denom;
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p2 = (p2 + dt*z2)./denom;
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divp = p1 - p1(:,il) + p2 - p2(iu,:);
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end
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elseif length(N) == 3 % Vectorial TV denoising
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repchannel = ones(N(3),1);
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while norm(divp(:) - lastdivp(:),inf) > Tol
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lastdivp = divp;
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z = divp - f*lambda;
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z1 = z(:,ir,:) - z;
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z2 = z(id,:,:) - z;
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denom = 1 + dt*sqrt(sum(z1.^2 + z2.^2,3));
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denom = denom(:,:,repchannel);
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p1 = (p1 + dt*z1)./denom;
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p2 = (p2 + dt*z2)./denom;
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divp = p1 - p1(:,il,:) + p2 - p2(iu,:,:);
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end
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end
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u = f - divp/lambda;
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