refactor: 项目目录结构重组

- 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
2026-07-16 20:59:58 +08:00
parent a720845948
commit 3ca079a980
100 changed files with 98 additions and 71 deletions
@@ -0,0 +1,85 @@
+function u = tvdenoise(f,lambda,Tol)
+%TVDENOISE  Total variation grayscale and color image denoising
+%   u = TVDENOISE(f,lambda) denoises the input image f.  The smaller
+%   the parameter lambda, the stronger the denoising.
+%
+%   The output u approximately minimizes the Rudin-Osher-Fatemi (ROF)
+%   denoising model
+%
+%       Min  TV(u) + lambda/2 || f - u ||^2_2,
+%        u
+%
+%   where TV(u) is the total variation of u.  If f is a color image (or any
+%   array where size(f,3) > 1), the vectorial TV model is used,
+%
+%       Min  VTV(u) + lambda/2 || f - u ||^2_2.
+%        u
+%
+%   TVDENOISE(...,Tol) specifies the stopping tolerance (default 1e-2).
+%
+%   The minimization is solved using Chambolle's method,
+%      A. Chambolle, "An Algorithm for Total Variation Minimization and
+%      Applications," J. Math. Imaging and Vision 20 (1-2): 89-97, 2004. 
+%   When f is a color image, the minimization is solved by a generalization
+%   of Chambolle's method,
+%      X. Bresson and T.F. Chan,  "Fast Minimization of the Vectorial Total
+%      Variation Norm and Applications to Color Image Processing", UCLA CAM
+%      Report 07-25.
+%
+%   Example:
+%   f = double(imread('barbara-color.png'))/255;
+%   f = f + randn(size(f))*16/255;
+%   u = tvdenoise(f,12);
+%   subplot(1,2,1); imshow(f); title Input
+%   subplot(1,2,2); imshow(u); title Denoised
+
+% Pascal Getreuer 2007-2008
+
+if nargin < 3
+    Tol = 1e-2;
+end
+
+if lambda < 0
+    error('Parameter lambda must be nonnegative.');
+end
+
+dt = 0.25;
+
+N = size(f);
+id = [2:N(1),N(1)];
+iu = [1,1:N(1)-1];
+ir = [2:N(2),N(2)];
+il = [1,1:N(2)-1];
+p1 = zeros(size(f));
+p2 = zeros(size(f));
+divp = zeros(size(f));
+lastdivp = ones(size(f));
+
+if length(N) == 2           % TV denoising
+    while norm(divp(:) - lastdivp(:),inf) > Tol
+        lastdivp = divp;
+        z = divp - f*lambda;
+        z1 = z(:,ir) - z;
+        z2 = z(id,:) - z;
+        denom = 1 + dt*sqrt(z1.^2 + z2.^2);
+        p1 = (p1 + dt*z1)./denom;
+        p2 = (p2 + dt*z2)./denom;
+        divp = p1 - p1(:,il) + p2 - p2(iu,:);
+    end
+elseif length(N) == 3       % Vectorial TV denoising
+    repchannel = ones(N(3),1);
+    
+    while norm(divp(:) - lastdivp(:),inf) > Tol
+        lastdivp = divp;
+        z = divp - f*lambda;
+        z1 = z(:,ir,:) - z;
+        z2 = z(id,:,:) - z;
+        denom = 1 + dt*sqrt(sum(z1.^2 + z2.^2,3));
+        denom = denom(:,:,repchannel);
+        p1 = (p1 + dt*z1)./denom;
+        p2 = (p2 + dt*z2)./denom;
+        divp = p1 - p1(:,il,:) + p2 - p2(iu,:,:);
+    end
+end
+
+u = f - divp/lambda;