create autoencoder for watemark deleting in matlab

To create an autoencoder for watermark deleting in Matlab, we need to follow the steps below:

Load the watermarked image and its corresponding original image.
Create an encoder network with a few convolutional layers and max-pooling layers. The number of filters in each convolutional layer should be increased gradually, and the max-pooling layer reduces the spatial dimensions of the feature map.
Create a decoder network by using transpose convolutional layers, which increase the spatial dimensions of the feature map, followed by a few convolutional layers without max-pooling. The number of filters should be decreased gradually to generate the output image.
Use the loaded pairs of images to train the autoencoder by minimizing the mean squared error (MSE) between the input and output images.
Use the trained autoencoder to remove the watermark from the input watermarked image.

Below is an example code that implements these steps:


main.m
% Load the watermarked image and its corresponding original image
watermarked = imread('watermarked_image.png');
original = imread('original_image.png');

% Create an encoder network
encoderLayers = [
    imageInputLayer([size(watermarked,1) size(watermarked,2) 3])
    convolution2dLayer(3,16,'Padding','same')
    reluLayer()
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    reluLayer()
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,64,'Padding','same')
    reluLayer()
    maxPooling2dLayer(2,'Stride',2)];

% Create a decoder network
decoderLayers = [
    transposedConv2dLayer(2,64,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    reluLayer()
    transposedConv2dLayer(2,32,'Stride',2)
    convolution2dLayer(3,16,'Padding','same')
    reluLayer()
    transposedConv2dLayer(2,3,'Stride',2)
    regressionLayer()];

% Combine the encoder and decoder networks to form an autoencoder
autoencoder = [
    encoderLayers
    decoderLayers];

% Train the autoencoder using the pairs of images
options = trainingOptions('adam', ...
    'InitialLearnRate', 0.001, ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 128, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false, ...
    'Plots', 'training-progress');
net = trainNetwork(watermarked,original,autoencoder,options);

% Use the trained autoencoder to remove the watermark from a new watermarked image
newWatermarked = imread('watermarked_image2.png');
output = predict(net,newWatermarked);
imshow(output);
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Note: This is just an example code to give you an idea of how to create an autoencoder for watermark deleting in Matlab. You may need to adjust the hyperparameters and network structure according to your specific task and dataset.

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