matlab中tv函数的代码

以下是MATLAB中实现全变分图像融合的tv函数的代码：

function [grad, mean] = tv(img, lambda, iter)
% 输入：
%   img: 要进行全变分的图像，可以是灰度图或彩色图，大小为m*n或m*n*3
%   lambda: 正则化参数，默认值为0.1
%   iter: 迭代次数，默认值为100
% 输出：
%   grad: 图像的梯度，大小为m*n或m*n*3
%   mean: 图像的平均值，大小为m*n或m*n*3

% 获取图像大小
[m, n, c] = size(img);

% 如果是灰度图，将其转换为三通道图像
if c == 1
    img = cat(3, img, img, img);
end

% 初始化grad和mean
grad = img;
mean = img;

% 设置默认参数
if nargin < 2
    lambda = 0.1;
end
if nargin < 3
    iter = 100;
end

% 计算梯度和平均值
for i = 1:iter
    % 计算梯度
    grad = grad - lambda*div(grad);
    
    % 计算平均值
    for j = 1:3
        mean(:,:,j) = meanfilt2(mean(:,:,j));
    end
end

% 将梯度和平均值分别归一化到[0,1]范围
grad = (grad - min(grad(:))) / (max(grad(:)) - min(grad(:)));
mean = (mean - min(mean(:))) / (max(mean(:)) - min(mean(:)));

end

% 计算梯度的函数
function d = div(grad)
% 计算梯度的div函数，使用差分计算梯度

[m, n, c] = size(grad);
if c == 1
    dy = diff(grad, 1, 1);
    dx = diff(grad, 1, 2);
    d = zeros(m, n);
    d(1:m-1,:) = d(1:m-1,:) + dy;
    d(2:m,:) = d(2:m,:) - dy;
    d(:,1:n-1) = d(:,1:n-1) + dx;
    d(:,2:n) = d(:,2:n) - dx;
else
    dy = diff(grad, 1, 1);
    dx = diff(grad, 1, 2);
    d = zeros(m, n, 3);
    d(1:m-1,:,1) = d(1:m-1,:,1) + dy(:,:,1);
    d(2:m,:,1) = d(2:m,:,1) - dy(:,:,1);
    d(:,1:n-1,1) = d(:,1:n-1,1) + dx(:,:,1);
    d(:,2:n,1) = d(:,2:n,1) - dx(:,:,1);
    d(1:m-1,:,2) = d(1:m-1,:,2) + dy(:,:,2);
    d(2:m,:,2) = d(2:m,:,2) - dy(:,:,2);
    d(:,1:n-1,2) = d(:,1:n-1,2) + dx(:,:,2);
    d(:,2:n,2) = d(:,2:n,2) - dx(:,:,2);
    d(1:m-1,:,3) = d(1:m-1,:,3) + dy(:,:,3);
    d(2:m,:,3) = d(2:m,:,3) - dy(:,:,3);
    d(:,1:n-1,3) = d(:,1:n-1,3) + dx(:,:,3);
    d(:,2:n,3) = d(:,2:n,3) - dx(:,:,3);
end

end

% 二维均值滤波函数
function img_filt = meanfilt2(img)
% img: 输入图像，大小为m*n
% img_filt: 滤波后的图像，大小为m*n

% 定义滤波器
h = fspecial('average', 3);

% 对图像进行滤波
img_filt = imfilter(img, h, 'replicate');

end

其中，div函数用于计算梯度的div算子，meanfilt2函数用于计算平均值的二维均值滤波函数。