Criminisi 图像修复算法 MATLAB 实现详解

一、算法实现代码

%% Criminisi 图像修复算法（支持彩色图像）
% 功能：修复图像中的缺失区域（如划痕、遮挡物）
clear; clc; close all;

%% 参数设置
imgPath = 'damaged_image.jpg'; % 输入图像路径
maskPath = 'mask.png'; % 缺失区域掩膜（白色为缺失）
patchSize = 9; % 修复块大小（奇数）
searchWin = 50; % 搜索窗口大小
maxIter = 1000; % 最大迭代次数

%% 读取图像与掩膜
img = imread(imgPath);
if size(img,3) == 1
    img = cat(3, img, img, img); % 转换为 RGB 格式
end
mask = imbinarize(im2double(imread(maskPath))); % 二值化掩膜

%% 初始化参数
[h,w,~] = size(img);
confidence = double(~mask); % 置信度图（已知区域=1）
isFixed = false(h,w); % 固定点标记
fixedPts = detectBoundary(mask); % 检测边界点作为初始修复点

%% 主循环
tic;
for iter = 1:maxIter
    if isempty(fixedPts)
        break;
    end
    % 选择优先级最高的点
    [p, fixedPts] = selectPriorityPoint(img, mask, confidence, fixedPts);
    % 搜索最佳匹配块
    [bestX, bestY] = findBestMatch(img, mask, p, patchSize, searchWin);
    % 执行修复
    [img, mask, confidence] = inpaintBlock(img, mask, confidence, bestX, bestY, patchSize);
    % 更新进度
    fprintf('Iteration %d: Repaired %d points\n', iter, size(fixedPts,1));
end
toc;

%% 结果可视化
figure;
subplot(1,2,1);
imshow(img);
title('原始图像');
subplot(1,2,2);
imshow(mask);
title('缺失区域掩膜');
figure;
imshow(img);
title('修复结果');

%% 核心函数定义
function fixedPts = detectBoundary(mask)
    % 检测掩膜边界点作为初始修复点
    [rows,cols] = find(mask);
    boundaryPts = [];
    for i = 1:length(rows)
        % 获取 8 邻域
        neighbors = [rows(i)+(-1:1), cols(i)+(-1:1)];
        valid = neighbors(all(neighbors > 0 & neighbors <= size(mask)), :);
        if any(mask(valid(:,1), valid(:,2)) == 0)
            boundaryPts = [boundaryPts; rows(i), cols(i)];
        end
    end
    fixedPts = boundaryPts;
end

function [p, fixedPts] = selectPriorityPoint(img, mask, confidence, fixedPts)
    % 选择优先级最高的点
    if isempty(fixedPts)
        [~,idx] = max(confidence(:));
        p = ind2sub(size(confidence), idx);
        return;
    end
    % 计算数据项（梯度方向）
    [Gx,Gy] = gradient(double(rgb2gray(img)));
    G = cat(3, Gx, Gy);
    normals = cat(3, -Gy, Gx);
    % 等照度线法向量
    maxP = -inf;
    p = [0,0];
    for i = 1:size(fixedPts,1)
        y = fixedPts(i,1);
        x = fixedPts(i,2);
        % 计算法向量
        if y==1 || y==size(img,1) || x==1 || x==size(img,2)
            n = normals(:,:,1); % 边界处法向量
        else
            n = normals(y,x,:);
        end
        % 计算数据项
        patch = getPatch(img, x,y,patchSize);
        knownPatch = getPatch(img, x,y,patchSize).*double(~mask);
        D = 1 - ssim(knownPatch, patch); % 计算优先级
        C = mean(confidence(max(1,y-floor(patchSize/2)):min(size(img,1),y+floor(patchSize/2)),...
            max(1,x-floor(patchSize/2)):min(size(img,2),x+floor(patchSize/2))));
        P = C * D;
        if P > maxP
            maxP = P;
            p = [y,x];
        end
    end
end

function [bestX, bestY] = findBestMatch(img, mask, p, patchSize, searchWin)
    % 在搜索窗口内寻找最佳匹配块
    [h,w] = size(img);
    y = p(1);
    x = p(2);
    % 定义搜索范围
    yMin = max(1, y-floor(searchWin/2));
    yMax = min(h, y+floor(searchWin/2));
    xMin = max(1, x-floor(searchWin/2));
    xMax = min(w, x+floor(searchWin/2));
    minSSD = inf;
    bestY = y;
    bestX = x;
    for yy = yMin:yMax
        for xx = xMin:xMax
            if mask(yy,xx) == 1
                continue; % 跳过缺失区域
            end
            % 计算 SSD
            targetPatch = getPatch(img, y,x,patchSize);
            candPatch = getPatch(img, yy,xx,patchSize);
            maskPatch = double(mask(y-floor(patchSize/2):y+floor(patchSize/2),...
                x-floor(patchSize/2):x+floor(patchSize/2)));
            ssd = sum(sum((targetPatch - candPatch).^2.*(1-maskPatch)));
            if ssd < minSSD
                minSSD = ssd;
                bestY = yy;
                bestX = xx;
            end
        end
    end
end

function [img, mask, confidence] = inpaintBlock(img, mask, confidence, x, y, patchSize)
    % 修复块操作
    halfPatch = floor(patchSize/2);
    yMin = max(1, y-halfPatch);
    yMax = min(size(img,1), y+halfPatch);
    xMin = max(1, x-halfPatch);
    xMax = min(size(img,2), x+halfPatch);
    % 复制匹配块内容
    targetPatch = getPatch(img, y,x,patchSize);
    maskPatch = double(mask(yMin:yMax, xMin:xMax));
    img(yMin:yMax, xMin:xMax,:) = img(yMin:yMax, xMin:xMax,:).*(1-maskPatch) + ... targetPatch .* maskPatch;
    % 更新置信度
    confidence(yMin:yMax, xMin:xMax) = confidence(yMin:yMax, xMin:xMax)*0.9;
    confidence(y,x) = 1; % 新修复点置信度设为 1
    % 更新掩膜
    mask(yMin:yMax, xMin:xMax) = 0;
end

function patch = getPatch(img, x, y, patchSize)
    % 提取图像块
    half = floor(patchSize/2);
    yMin = max(1, y-half);
    yMax = min(size(img,1), y+half);
    xMin = max(1, x-half);
    xMax = min(size(img,2), x+half);
    patch = img(yMin:yMax, xMin:xMax,:);
end