基于块匹配的MATLAB视频去抖动算法

一、核心代码

1. 视频读取与预处理

matlab 复制代码

%% 视频参数设置
videoFile = 'input.mp4';
reader = vision.VideoFileReader(videoFile, 'ImageColorSpace', 'Intensity');
frameSize = [reader.VideoSize(2), reader.VideoSize(1)]; % [height, width]

%% 初始化模块
smoothWinSize = 5;    % 运动滤波窗口大小
maxSearchRange = 8;   % 最大搜索范围
blockSize = 16;       % 块大小

2. 块匹配运动估计

matlab 复制代码

function motionVectors = block_matching(prevFrame, currFrame)
    [H, W] = size(prevFrame);
    numBlocksY = H/blockSize;
    numBlocksX = W/blockSize;
    
    motionVectors = zeros(numBlocksY, numBlocksX, 2);
    
    for i = 1:numBlocksY
        for j = 1:numBlocksX
            % 定义搜索窗口
            refBlock = prevFrame( (i-1)*blockSize+1 : i*blockSize, ...
                                 (j-1)*blockSize+1 : j*blockSize );
            
            % 搜索范围限制
            xRange = max(1, j-1-blockSize) : min(numBlocksX, j-1+blockSize);
            yRange = max(1, i-1-blockSize) : min(numBlocksY, i-1+blockSize);
            
            % 计算匹配代价
            minCost = inf;
            bestOffset = [0,0];
            
            for m = yRange
                for n = xRange
                    targetBlock = currFrame( (m-1)*blockSize+1 : m*blockSize, ...
                                            (n-1)*blockSize+1 : n*blockSize );
                    cost = sum(sum(abs(refBlock - targetBlock)));
                    
                    if cost < minCost
                        minCost = cost;
                        bestOffset = [m-i, n-j];
                    end
                end
            end
            
            motionVectors(i,j,:) = bestOffset;
        end
    end
end

3. 抖动识别与滤波

matlab 复制代码

function filteredVectors = stabilize_motion(motionVectors)
    % 中值滤波去除异常值
    filteredVectors(:,:,1) = medfilt2(motionVectors(:,:,1), [smoothWinSize smoothWinSize]);
    filteredVectors(:,:,2) = medfilt2(motionVectors(:,:,2), [smoothWinSize smoothWinSize]);
    
    % 自适应运动平滑
    dt = 0.1; % 时间步长
    for i = 3:size(filteredVectors,1)-2
        for j = 3:size(filteredVectors,2)-2
            % 二次曲线拟合
            A = [1, filteredVectors(i-1,j,1), filteredVectors(i-1,j,2);
                 1, filteredVectors(i,j,1), filteredVectors(i,j,2);
                 1, filteredVectors(i+1,j,1), filteredVectors(i+1,j,2);
                 1, filteredVectors(i+2,j,1), filteredVectors(i+2,j,2)];
            b = [i-1; i; i+1; i+2]';
            coeffs = A\b;
            filteredVectors(i,j,1) = coeffs(2);
            filteredVectors(i,j,2) = coeffs(3);
        end
    end
end

4. 运动补偿与视频生成

matlab 复制代码

%% 视频处理循环
writer = vision.VideoWriter('output.avi', 'FrameRate', reader.FrameRate);
open(writer);

prevFrame = readFrame(reader);
prevGray = rgb2gray(prevFrame);

while ~isDone(reader)
    currFrame = readFrame(reader);
    currGray = rgb2gray(currFrame);
    
    % 运动估计
    motion = block_matching(prevGray, currGray);
    
    % 抖动滤波
    smoothMotion = stabilize_motion(motion);
    
    % 运动补偿
    [H,W] = size(currGray);
    stabilized = zeros(H,W);
    
    for i = 1:blockSize:H
        for j = 1:blockSize:W
            % 计算目标位置
            offset = round(smoothMotion(ceil(i/blockSize), ceil(j/blockSize), :));
            x = j + offset(2);
            y = i + offset(1);
            
            % 边界处理
            x = max(1, min(x, W-blockSize+1));
            y = max(1, min(y, H-blockSize+1));
            
            % 块复制
            stabilized(i:i+blockSize-1, j:j+blockSize-1) = ...
                currGray(y:y+blockSize-1, x:x+blockSize-1);
        end
    end
    
    % 写入输出视频
    step(writer, stabilized);
    
    % 更新参考帧
    prevGray = currGray;
end

close(writer);

二、优化

1. 运动估计加速

金字塔搜索：采用图像金字塔减少搜索范围

matlab 复制代码

function pyr = build_pyramid(img, levels)
    pyr = cell(levels,1);
    pyr{1} = img;
    for l=2:levels
        pyr{l} = imresize(pyr{l-1}, 0.5);
    end
end

快速SAD计算：使用积分图像加速块匹配

matlab 复制代码

function cost = fast_sad(ref, target)
    [H,W] = size(ref);
    integral = cumsum(cumsum(target));
    for i=1:H
        for j=1:W
            x1 = max(1, j-blockSize+1);
            x2 = min(W, j+blockSize-1);
            y1 = max(1, i-blockSize+1);
            y2 = min(H, i+blockSize-1);
            sumRef = integral(y2,y2) - integral(y1-1,y2) - ...
                    integral(y2,y1-1) + integral(y1-1,y1-1);
            cost(i,j) = sum(sum(abs(ref(i:i+blockSize-1,j:j+blockSize-1) - ...
                                    target(y1:y2,x1:x2))));
        end
    end
end

2. 抖动滤波改进

自适应滤波窗口：根据运动幅度动态调整窗口大小

matlab 复制代码

function winSize = adaptive_window(motionMag)
    meanMag = mean(motionMag(:));
    if meanMag < 2
        winSize = 3;
    elseif meanMag < 5
        winSize = 5;
    else
        winSize = 7;
    end
end

运动轨迹平滑：采用卡尔曼滤波优化运动估计

matlab 复制代码

function [x_est, P] = kalman_filter(x_prev, P_prev, z)
    % 状态转移矩阵
    F = [1 1; 0 1];
    % 过程噪声协方差
    Q = diag([0.1, 0.1]);
    % 测量矩阵
    H = eye(2);
    % 测量噪声协方差
    R = diag([1, 1]);

    % 预测步骤
    x_pred = F * x_prev;
    P_pred = F * P_prev * F' + Q;

    % 更新步骤
    K = P_pred * H' / (H * P_pred * H' + R);
    x_est = x_pred + K * (z - H * x_pred);
    P = (eye(2) - K * H) * P_pred;
end

参考代码利用块匹配稳像算法实现视频去抖动 www.youwenfan.com/contentcsi/65174.html

三、典型应用场景

手持设备拍摄：消除步行拍摄的抖动（PSNR提升4-6dB）
车载摄像头：补偿车辆行驶中的高频振动
无人机航拍：抑制飞行器姿态变化导致的画面晃动