nmr sequences contain noise as a cnn input and the corresponding clean nmr sequences ad the output in matlab in matlab

To use noisy NMR sequences as a CNN input and clean NMR sequences as the output, you can use a deep learning framework in MATLAB, such as Deep Learning Toolbox. Here's the general workflow:

  1. Load the noisy and clean NMR datasets into MATLAB using appropriate functions (e.g. load or readtable).
  2. Preprocess the data as needed, such as normalizing or scaling the data.
  3. Split the dataset into training, validation, and test sets using crossvalind function.
  4. Design and train a convolutional neural network using trainNetwork function with the noisy sequences as inputs and clean sequences as outputs.
  5. Evaluate the trained neural network using predict function on the test set.
  6. Visualize the denoising performance of the trained neural network by comparing the predicted clean sequences with the ground truth clean sequences using appropriate metrics such as signal-to-noise ratio (SNR) or peak signal-to-noise ratio (PSNR).

Here's a sample code snippet to get you started:

main.m
% Load the noisy and clean NMR datasets
noisy_data = readtable('noisy_data.csv');
clean_data = readtable('clean_data.csv');

% Preprocess the data
X = noisy_data.Data;
Y = clean_data.Data;
X = normalize(X, 'range');

% Split the dataset into training, validation, and test sets
cv = cvpartition(size(X,1),'HoldOut',0.2);
idx = cv.test;
train_X = X(~idx,:);
train_Y = Y(~idx,:);
test_X = X(idx,:);
test_Y = Y(idx,:);

% Design and train a convolutional neural network
layers = [ ...
    imageInputLayer([1 size(X,2) 1])
    convolution2dLayer([1 3], 16, 'Padding', 'same')
    reluLayer
    convolution2dLayer([1 3], 8, 'Padding', 'same')
    reluLayer
    convolution2dLayer([1 3], 1, 'Padding', 'same')
    regressionLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'ValidationData',{test_X, test_Y}, ...
    'Plots','training-progress');
net = trainNetwork(train_X, train_Y, layers, options);

% Evaluate the trained neural network
predicted_Y = predict(net, test_X);

% Visualize the denoising performance
figure; imshowpair(test_Y, predicted_Y, 'montage');
diff = test_Y - predicted_Y;
figure; plot(1:size(diff,1),diff);
title(sprintf('PSNR: %.2f dB', psnr(test_Y, predicted_Y)));

1210 chars
41 lines

similar matlab code snippets

nmr sequences contain noise as a cnn input and the corresponding sequences ad the output in matlab
a time signal use resnet denoise in matlab
a signal use resnet denoise in matlab
denoise use resnet in matlab
1d resnet in matlab
dtft in matlab
chemical process fault detection using deep learning in matlab
downconvert a signal in matlab
implement a custom self attention layer in matlab in matlab
down convert a signal in matlab

related categories

matlab
signal-processing
deep-learning
convolutional-neural-networks
denoising

gistlibby LogSnag