To create an autoencoder for watermark deletion from RGB pictures in MATLAB, we can follow these steps:
Here's a sample code snippet that demonstrates how to create an autoencoder for watermark deletion using MATLAB:
% 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);
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.