gistlib - autoencoder layers for best picture quality in matlab

To create an autoencoder with the goal of achieving the best picture quality, we can use the following steps:

Load the image dataset and pre-process it using the image processing tools in Matlab, such as imread, imresize, and imadjust.


main.m
% Load and preprocess the image dataset
imds = imageDatastore('path/to/dataset');
augmenter = imageDataAugmenter('RandRotation',[0 360],'RandXReflection',true,'RandYReflection',true);
preprocessed_imds = augmentedImageDatastore([227 227], imds, 'DataAugmentation', augmenter);
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Define the architecture of the autoencoder using the neural network toolbox in Matlab. We can use a two-layer autoencoder with a bottleneck layer in the middle, where the number of nodes in the bottleneck layer is smaller than the number of nodes in the input and output layers.


main.m
% Create an autoencoder
encoder_layers = [
    imageInputLayer([227 227 3])
    convolution2dLayer(3, 16, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 8, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 4, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(8)
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(4*4*8)
    reluLayer];

decoder_layers = [
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(8)
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(4*4*8)
    reluLayer
    reshapeLayer([4 4 8])
    transposedConv2dLayer(3, 4, 'Stride', 2, 'Padding', 'same')
    reluLayer
    transposedConv2dLayer(3, 8, 'Stride', 2, 'Padding', 'same')
    reluLayer
    transposedConv2dLayer(3, 16, 'Stride', 2, 'Padding', 'same')
    reluLayer
    convolution2dLayer(3, 3, 'Padding', 'same')
    ];

autoencoder_layers = [
    encoder_layers
    decoder_layers];

autoencoder = trainAutoencoder(preprocessed_imds, autoencoder_layers);
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Train the autoencoder using the preprocessed image dataset and the defined autoencoder architecture. We can use the trainAutoencoder function in Matlab to train the autoencoder. The output of the function is the trained autoencoder model.


main.m
% Train the autoencoder
options = trainingOptions('adam', ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 32, ...
    'InitialLearnRate', 1e-3, ...
    'Shuffle', 'every-epoch', ...
    'Plots', 'training-progress');
autoencoder = trainAutoencoder(preprocessed_imds, autoencoder_layers, options);
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Test the trained autoencoder model by reconstructing some images from the test set and comparing the reconstructed images with the original ones using the image processing tools in Matlab, such as imshow and psnr.


main.m
% Test the trained autoencoder
test_imds = imageDatastore('path/to/test/set');
preprocessed_test_imds = augmentedImageDatastore([227 227], test_imds);

test_set_reconstructed = predict(autoencoder, preprocessed_test_imds);
test_set_reconstructed = denormalizeImages(test_set_reconstructed);

for i = 1:numel(test_imds.Files)
    test_image = readimage(test_imds, i);
    reconstructed_image = test_set_reconstructed{i};

    figure;
    subplot(1,2,1);
    imshow(test_image);
    title('Original image');
    subplot(1,2,2);
    imshow(reconstructed_image);
    title('Reconstructed image');
    
    psnr_val = psnr(reconstructed_image, test_image);
    fprintf('PSNR value for image %d: %.2f dB\n', i, psnr_val);
end
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By following these steps, we can create an autoencoder in Matlab that achieves the best picture quality.

autoencoder layers for best picture quality in matlab

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