能生成一个DenseNet网络用于图像去噪的matlab代码

当然能！以下是基于 DenseNet 的图像去噪 Matlab 代码：

function [output_image] = denoise_image(input_image)
% define parameters
num_channels = size(input_image, 3);
num_filters = 32;
growth_rate = 12;
num_blocks = 4;
block_depths = [6, 12, 24, 16];
dropout_rate = 0.2;

% define DenseNet architecture
input_layer = imageInputLayer([size(input_image, 1) size(input_image, 2) num_channels]);
prev_layer = input_layer;
for i = 1:num_blocks
    [prev_layer, num_filters] = dense_layer(prev_layer, num_filters, growth_rate, ...
        block_depths(i), dropout_rate, strcat('dense_', num2str(i)));
end
output_layer = convolution2dLayer(1, num_channels, 'Padding', 'same', 'Name', 'output_layer');
prev_layer = output_layer(prev_layer);

% define training options
options = trainingOptions('sgdm', 'InitialLearnRate', 0.001, ...
    'MaxEpochs', 100, 'MiniBatchSize', 32, ...
    'Shuffle', 'every-epoch', 'Plots', 'training-progress');

% train the network
trained_net = trainNetwork(input_image, prev_layer, options);

% denoise the input image
output_image = predict(trained_net, input_image);

end

function [out_layer, num_filters] = dense_layer(in_layer, num_filters, ...
    growth_rate, num_layers, dropout_rate, layer_name)
for i = 1:num_layers
    % Batch normalization layer
    batch_norm_layer = batchNormalizationLayer('Name', strcat(layer_name, '_BN_', num2str(i)));
    relu_layer = reluLayer('Name', strcat(layer_name, '_ReLU_', num2str(i)));
    conv_layer = convolution2dLayer(3, num_filters, 'Padding', 'same', ...
        'Name', strcat(layer_name,'_Conv_',num2str(i)));
    dropout_layer = dropoutLayer(dropout_rate, 'Name', strcat(layer_name, '_Dropout_', num2str(i)));

    % connect layers
    out_layer = batch_norm_layer(in_layer);
    out_layer = relu_layer(out_layer);
    out_layer = conv_layer(out_layer);
    out_layer = dropout_layer(out_layer);

    % concatenate output of current layer with input
    in_layer = cat(3, in_layer, out_layer);

    % update number of filters
    num_filters = num_filters + growth_rate;
end
end

以上代码定义了一个 DenseNet 网络，包含了 4 个 Dense 层和一个输出层。每个 Dense 层由若干个卷积层、BN 层、ReLU 层和 Dropout 层组成，输入是前一层的输出和网络的输入。输出层是一个卷积层，输出与输入的通道数相同。通过训练网络可以得到一个去噪模型，用于去除输入图像中的噪声。