criminisi算法实现污损车牌的修复的代码实现

Criminisi算法是一种经典的图像修复算法，可以用于污损车牌的修复。下面给出一份Matlab代码实现，供参考：

function [I_repaired] = criminisi(I_dirty, mask)
% 输入：I_dirty为污损图像，mask为污损区域的二值掩膜
% 输出：I_repaired为修复后的图像
% 参考论文：A. Criminisi, P. Perez, and K. Toyama, “Object removal by exemplar-based inpainting,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2003, pp. 721–728.

% 超参数设置
patch_size = 9;      % 修复时使用的局部块大小
search_size = 25;    % 用于搜索最佳匹配块的局部搜索区域大小
alpha = 0.5;         % 修复优先级中的权重参数
eps = 0.1;           % 修复优先级中的平滑参数
max_iter = 5000;     % 迭代次数上限

% 初始化
[I_height, I_width, I_channels] = size(I_dirty);
I_repaired = I_dirty;
mask = double(mask);
[rows, cols] = find(mask > 0);
num_pixels = size(rows, 1);
num_channels = size(I_dirty, 3);
fill_front = bwperim(mask, 8);  % 填充前沿，表示污损区域和非污损区域的交界
fill_front_indices = find(fill_front);
unknown_indices = find(mask == 0);

% 用于计算修复优先级的函数
compute_priority = @(inpaint_region, confidence_region, texture_region) ...
    abs(conv2(inpaint_region, [-1 1], 'same')) + ...
    abs(conv2(inpaint_region, [-1; 1], 'same')) + ...
    alpha * (eps + conv2(confidence_region, ones(patch_size), 'same')) ./ (eps + conv2(ones(size(confidence_region)), ones(patch_size), 'same')) + ...
    (1 - alpha) * (eps + conv2(texture_region, ones(patch_size), 'same')) ./ (eps + conv2(ones(size(texture_region)), ones(patch_size), 'same'));

% 迭代修复
for iter = 1:max_iter
    disp(['Iteration: ', num2str(iter)]);
    if isempty(fill_front_indices)
        break;
    end
    
    % 计算当前填充前沿中每个像素的优先级
    priority_map = zeros(I_height, I_width);
    for i = 1:num_pixels
        row = rows(i);
        col = cols(i);
        inpaint_region = I_repaired(row-patch_size+1:row+patch_size-1, col-patch_size+1:col+patch_size-1, :);
        confidence_region = mask(row-patch_size+1:row+patch_size-1, col-patch_size+1:col+patch_size-1);
        texture_region = stdfilt(rgb2gray(I_repaired(row-search_size:row+search_size, col-search_size:col+search_size, :)), ones(patch_size));
        priority_map(row, col) = compute_priority(inpaint_region, confidence_region, texture_region);
    end
    
    % 找到当前填充前沿中优先级最高的像素
    [max_priority, max_index] = max(priority_map(fill_front_indices));
    max_index = fill_front_indices(max_index);
    [max_row, max_col] = ind2sub([I_height, I_width], max_index);
    
    % 从周围的非污损区域中搜索最佳匹配块
    search_region = I_repaired(max_row-search_size:max_row+search_size, max_col-search_size:max_col+search_size, :);
    search_mask = mask(max_row-search_size:max_row+search_size, max_col-search_size:max_col+search_size);
    [best_patch, best_row, best_col] = find_best_match(I_dirty, search_region, search_mask, [max_row, max_col], patch_size);
    
    % 将找到的最佳匹配块复制到当前填充前沿中的像素位置
    I_repaired(max_row-patch_size+1:max_row+patch_size-1, max_col-patch_size+1:max_col+patch_size-1, :) = best_patch;
    mask(max_row-patch_size+1:max_row+patch_size-1, max_col-patch_size+1:max_col+patch_size-1) = 1;
    
    % 更新填充前沿
    fill_front = bwperim(mask, 8);
    fill_front_indices = find(fill_front);
end

end

% 从周围的非污损区域中搜索最佳匹配块
function [best_patch, best_row, best_col] = find_best_match(I_dirty, search_region, search_mask, center, patch_size)
kernel = ones(patch_size);
num_channels = size(I_dirty, 3);
min_error = Inf;
best_patch = zeros(patch_size, patch_size, num_channels);
best_row = center(1);
best_col = center(2);
for row = patch_size:size(search_region, 1)-patch_size
    for col = patch_size:size(search_region, 2)-patch_size
        if search_mask(row, col) == 0
            patch = search_region(row-patch_size+1:row+patch_size-1, col-patch_size+1:col+patch_size-1, :);
            error = sum(sum(sum(kernel .* (I_dirty(center(1)-patch_size+1:center(1)+patch_size-1, center(2)-patch_size+1:center(2)+patch_size-1, :) - patch).^2)));
            if error < min_error
                min_error = error;
                best_patch = patch;
                best_row = center(1) - (row - patch_size);
                best_col = center(2) - (col - patch_size);
            end
        end
    end
end
end

在这份代码中，我们实现了Criminisi算法中的核心步骤，包括计算修复优先级、搜索最佳匹配块、复制最佳匹配块到当前像素位置等。代码中还包括一些超参数的设置，如局部块大小、搜索区域大小、权重参数、平滑参数等。需要注意的是，这份代码仅供参考，实际应用时需要根据具体情况进行调整和优化。