Here's an example code to use the wavelet transform and CNN for the given problem:
% Load the data
clean_data = load('clean_NMR.mat');
noisy_data = load('noisy_NMR.mat');
% Define the wavelet parameters
wname = 'sym8';
level = 4;
% Perform wavelet decomposition and thresholding
for i = 1:size(noisy_data, 3)
% noisy data
[C, S] = wavedec2(noisy_data(:,:,i), level, wname);
T = thselect(C,'rigrsure');
C_t = wthresh(C,'h',T);
noisy_data_scaled = uint8(waverec2(C_t, S, wname));
% clean data
[C, S] = wavedec2(clean_data(:,:,i), level, wname);
T = thselect(C,'rigrsure');
C_t = wthresh(C,'h',T);
clean_data_scaled = uint8(waverec2(C_t, S, wname));
% store the data
noisy_data_scaled_all(:,:,i) = noisy_data_scaled;
clean_data_scaled_all(:,:,i) = clean_data_scaled;
end
% Split the data into training and validation dataset
[trained_cnn, validation_cnn] = trainRatio(0.7);
% Define the CNN architecture
input_layer = imageInputLayer([320 240 1]);
conv_layer1 = convolution2dLayer(3, 32,'Padding', 'same');
relu_layer1 = reluLayer();
max_pool1 = maxPooling2dLayer(2, 'Stride', 2);
conv_layer2 = convolution2dLayer(3, 64, 'Padding', 'same');
relu_layer2 = reluLayer();
max_pool2 = maxPooling2dLayer(2, 'Stride', 2);
fully_connected_layer = fullyConnectedLayer(2);
output_layer = regressionLayer();
layers = [input_layer;
conv_layer1;
relu_layer1;
max_pool1;
conv_layer2;
relu_layer2;
max_pool2;
fully_connected_layer;
output_layer];
% Define the training options
options = trainingOptions('adam', ...
'MaxEpochs', 10, ...
'MiniBatchSize', 50, ...
'Shuffle', 'every-epoch', ...
'ValidationData', validation_cnn, ...
'ValidationFrequency', 30, ...
'Verbose', false, ...
'Plots', 'training-progress');
% Train the CNN
trained_net = trainNetwork(noisy_data_scaled_all, clean_data_scaled_all, layers, options);
In this code, we first load the clean and noisy data. We then perform the wavelet decomposition and thresholding on both datasets. After pre-processing, we split the data and define the CNN architecture. Finally, we define the training options and train the CNN using trainNetwork.
Note: The above code is a sample and may require modification according to the size, feature-set of the NMR dataset.