matlab盲元簇修复代码

盲元簇修复是一种针对图像缺失的修复方法，可以使用以下 Matlab 代码实现：

% 读取图像
img = imread('your_image_path');

% 将图像转换为灰度图像
gray_img = rgb2gray(img);

% 生成掩膜，模拟图像缺失
missing_mask = rand(size(gray_img)) > 0.5;
missing_img = gray_img;
missing_img(missing_mask) = 0;

% 盲元簇修复
recovered_img = blind_cluster_recover(missing_img, missing_mask);

% 显示图像
figure;
subplot(1,3,1);
imshow(gray_img);
title('Original Image');
subplot(1,3,2);
imshow(missing_img);
title('Image with Missing Pixels');
subplot(1,3,3);
imshow(recovered_img);
title('Recovered Image');

function [recovered_img] = blind_cluster_recover(missing_img, missing_mask)
    % 定义参数
    patch_size = 7;
    patch_num = 200;
    cluster_num = 64;
    max_iter = 100;

    % 初始化
    [height, width] = size(missing_img);
    recovered_img = missing_img;
    cluster_centers = zeros(cluster_num, patch_size^2);

    % 提取块和聚类
    for i = 1:max_iter
        % 提取块
        patches = extract_patches(recovered_img, missing_mask, patch_size, patch_num);

        % 训练聚类器
        cluster_centers = kmeans(patches', cluster_num);

        % 重建图像
        recovered_img = reconstruct_image(recovered_img, missing_mask, cluster_centers, patch_size);
    end
end

function [patches] = extract_patches(img, mask, patch_size, patch_num)
    % 随机选择块位置
    [height, width] = size(img);
    patch_indices = find(~mask);
    patch_indices = patch_indices(randperm(length(patch_indices)));
    patch_indices = patch_indices(1:patch_num);

    % 提取块
    patches = zeros(patch_size^2, patch_num);
    for i = 1:patch_num
        [row, col] = ind2sub([height, width], patch_indices(i));
        patch = img(row-floor(patch_size/2):row+floor(patch_size/2), col-floor(patch_size/2):col+floor(patch_size/2));
        patches(:,i) = patch(:);
    end
end

function [recovered_img] = reconstruct_image(img, mask, cluster_centers, patch_size)
    % 定义参数
    [height, width] = size(img);
    patch_num = 200;
    cluster_num = size(cluster_centers, 1);
    patch_indices = find(~mask);

    % 重建图像
    recovered_img = img;
    for i = 1:length(patch_indices)
        [row, col] = ind2sub([height, width], patch_indices(i));
        patch = img(row-floor(patch_size/2):row+floor(patch_size/2), col-floor(patch_size/2):col+floor(patch_size/2));
        patch = patch(:);
        [~, cluster_idx] = min(sum((repmat(patch, 1, cluster_num)-cluster_centers').^2));
        cluster_patch = cluster_centers(cluster_idx,:)';
        cluster_patch = reshape(cluster_patch, [patch_size, patch_size]);
        recovered_img(row, col) = cluster_patch(floor(patch_size/2)+1, floor(patch_size/2)+1);
    end
end

在代码中，我们首先读取图像并将其转换为灰度图像。然后，我们生成一个随机掩膜，模拟图像缺失。接着，我们调用 `blind_cluster_recover` 函数进行盲元簇修复。该函数首先定义了一些参数，如块大小、块数量、聚类中心数量和最大迭代次数。然后，该函数初始化图像并进入一个循环，每个循环中提取块、训练聚类器并重建图像。最后，我们在一个图像窗口中显示原始图像、缺失像素图像和恢复图像。

该代码中使用的 `extract_patches` 函数用于从图像中提取块，并使用 `kmeans` 函数进行聚类。`reconstruct_image` 函数用于重建图像。