create autoencoder for watemark deleting from rgb pictures in matlab

To create an autoencoder for watermark deletion from RGB pictures in MATLAB, we can follow these steps:

  1. Load the watermark+image data
  2. Preprocess the data by normalizing the pixel values to [0, 1]
  3. Split the data into training and testing sets
  4. Define the autoencoder model using the Deep Learning Toolbox in MATLAB
  5. Train the autoencoder using the training set
  6. Evaluate the performance of the autoencoder using the testing set
  7. Use the trained autoencoder to remove watermark from an input image.

Here's a sample code snippet that demonstrates how to create an autoencoder for watermark deletion using MATLAB:

main.m
% Load watermark+image data
data = load('watermark_data.mat');
x_train = data.x_train;
x_test = data.x_test;

% Preprocess the data
x_train_norm = double(x_train) / 255.0;
x_test_norm = double(x_test) / 255.0;

% Define the autoencoder model
encoder = [ ...
    imageInputLayer([256 256 3])
    convolution2dLayer(3, 32, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 16, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 8, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 8, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)];
decoder = [ ...
    transposedConv2dLayer(2, 8, 'Stride', 2)
    reluLayer
    transposedConv2dLayer(2, 8, 'Stride', 2)
    reluLayer
    transposedConv2dLayer(3, 16, 'Stride', 2, 'Cropping', 'same')
    reluLayer
    transposedConv2dLayer(2, 32, 'Stride', 2)
    reluLayer
    transposedConv2dLayer(3, 3, 'Padding', 'same')
    sigmoidLayer];
autoencoder = [
    encoder
    decoder];

% Train the autoencoder
options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 64, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');
trained_autoencoder = trainNetwork(x_train_norm, x_train_norm, autoencoder, options);

% Evaluate the performance of the autoencoder
recon_test_norm = predict(trained_autoencoder, x_test_norm);
psnr_error = psnr(x_test_norm, recon_test_norm);
fprintf('Test PSNR = %f\n', psnr_error);

% Use the trained autoencoder to remove watermark from images
input_image = imread('watermarked_image.png');
input_image_norm = double(input_image) / 255.0;
output_image_norm = predict(trained_autoencoder, input_image_norm);
output_image = uint8(output_image_norm * 255.0);
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In this example, we define an autoencoder with several convolutional and transposed convolutional layers using the Deep Learning Toolbox in MATLAB. We train the autoencoder using the watermark+image data, and evaluate its performance using the testing set. Finally, we use the trained autoencoder to remove the watermark from an input image.

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