2000 nmr sequences instead of image contain noise wavelet coefficient as a cnn input and the corresponding clean nmr sequences wavelet coefficient as the output in matlab

Assuming that you have already obtained the wavelet coefficients for the noisy and clean NMR sequences, you can use a convolutional neural network (CNN) to perform denoising on your input data. Here's an example of how to set that up in MATLAB:

main.m
% Load the noisy and clean wavelet coefficients
noisy_coef = load('noisy_data.mat');
clean_coef = load('clean_data.mat');

% Define the CNN architecture
layers = [
    imageInputLayer([size(noisy_coef,1), size(noisy_coef,2), 1])
    convolution2dLayer(3, 16, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 8, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 1, 'Padding', 'same')
    regressionLayer
];

% Define the training options
options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 32, ...
    'ValidationData', {noisy_coef, clean_coef}, ...
    'Plots','training-progress');

% Train the neural network
net = trainNetwork(noisy_coef, clean_coef, layers, options);
855 chars
29 lines

Here, we first load the noisy and clean wavelet coefficients from their respective .mat files. Note that the noisy_coef and clean_coef matrices should have matching dimensions, with each pair of corresponding patches from the noisy and clean data sharing the same spatial location.

Next, we define the CNN architecture using the layer object, which consists of an input layer, two sets of convolutional, batch normalization, ReLU activation, and max pooling layers, and a final convolutional layer with a regression output.

We then use the trainingOptions object to set the optimizer (Adam optimization), number of epochs, mini-batch size, and validation data to be used during training, as well as to enable progress plots during the training process.

Finally, we train the network using the trainNetwork function, passing in the noisy and clean wavelet coefficients along with the network layers and training options. The resulting trained net object can then be used to denoise your NMR sequences by passing in the wavelet coefficients of the noisy sequences as input and receiving the denoised wavelet coefficients as output.