基于MATLAB的CNN大气散射传播率计算与图像去雾实现

% 示例代码：加载和预处理数据
imds = imageDatastore('hazy_images_folder');
pxds = imageDatastore('transmission_maps_folder', 'ReadFcn', @(filename) im2gray(imread(filename)));
​
% 将图像和透射率图组合为训练数据
trainingData = combine(imds, pxds);

function lgraph = createTransmissionNet(inputSize)
% 创建编码器-解码器网络用于透射率估计
​
    layers = [
        imageInputLayer(inputSize, 'Name', 'input')
        
        % 编码器
        convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1')
        reluLayer('Name', 'relu1')
        maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool1')
        
        convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2')
        reluLayer('Name', 'relu2')
        maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool2')
        
        convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv3')
        reluLayer('Name', 'relu3')
        
        % 解码器
        transposedConv2dLayer(2, 128, 'Stride', 2, 'Name', 'tconv1')
        reluLayer('Name', 'relu4')
        
        transposedConv2dLayer(2, 64, 'Stride', 2, 'Name', 'tconv2')
        reluLayer('Name', 'relu5')
        
        convolution2dLayer(3, 1, 'Padding', 'same', 'Name', 'final_conv')
        sigmoidLayer('Name', 'sigmoid')  % 透射率值应在0-1之间
    ];
    
    lgraph = layerGraph(layers);
end

% 定义训练选项
options = trainingOptions('adam', ...
    'InitialLearnRate', 1e-4, ...
    'MaxEpochs', 30, ...
    'MiniBatchSize', 8, ...
    'Shuffle', 'every-epoch', ...
    'Plots', 'training-progress', ...
    'Verbose', false);
​
% 训练网络
net = trainNetwork(trainingData, lgraph, options);

function A = estimateAtmosphericLight(hazyImage, transmissionMap)
% 估计大气光值
% 选择透射率最低的像素（雾最浓的区域）对应的有雾图像中最亮的像素
​
    [height, width] = size(transmissionMap);
    numPixels = height * width;
    numTopPixels = max(1, round(0.001 * numPixels));  % 前0.1%的像素
    
    % 将透射率图转换为向量并排序
    transmissionVector = reshape(transmissionMap, numPixels, 1);
    [~, sortedIndices] = sort(transmissionVector);
    
    % 选择透射率最低的像素
    darkestIndices = sortedIndices(1:numTopPixels);
    
    % 找到有雾图像中这些像素最亮的强度
    hazyVector = reshape(hazyImage, numPixels, 3);
    darkestPixels = hazyVector(darkestIndices, :);
    
    % 取这些像素中最亮的作为大气光值
    A = max(darkestPixels, [], 1);
end

function dehazedImage = recoverImage(hazyImage, transmissionMap, A)
% 根据大气散射模型恢复无雾图像
​
    % 确保透射率不会太小（避免除零和噪声放大）
    t = max(transmissionMap, 0.1);
    
    % 扩展透射率图到三通道
    t = repmat(t, [1, 1, 3]);
    
    % 根据大气散射模型复原图像
    dehazedImage = (hazyImage - A) ./ t + A;
    
    % 确保像素值在有效范围内
    dehazedImage = max(min(dehazedImage, 1), 0);
end

% 加载有雾图像
hazyImage = im2double(imread('hazy_image.jpg'));
​
% 使用训练好的网络估计透射率
transmissionMap = predict(net, hazyImage);
​
% 估计大气光值
A = estimateAtmosphericLight(hazyImage, transmissionMap);
​
% 复原图像
dehazedImage = recoverImage(hazyImage, transmissionMap, A);
​
% 显示结果
figure;
subplot(1, 3, 1); imshow(hazyImage); title('有雾图像');
subplot(1, 3, 2); imshow(transmissionMap); title('估计的透射率图');
subplot(1, 3, 3); imshow(dehazedImage); title('去雾结果');