To train a CNN to denoise NMR sequences using wavelet coefficients in Matlab, you can follow these steps:
- Load the noisy and clean NMR data:
- Compute the wavelet coefficients for both the noisy and clean NMR data using the wavelet transform:
- Extract patches of the wavelet coefficients and the corresponding clean wavelet coefficients:
- Reshape the patches to be compatible with a CNN:
- Define the CNN architecture for denoising:
layers = [
imageInputLayer([H W D])
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv1')
reluLayer('Name','relu1')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv2')
reluLayer('Name','relu2')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv3')
reluLayer('Name','relu3')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv4')
reluLayer('Name','relu4')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv5')
reluLayer('Name','relu5')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv6')
reluLayer('Name','relu6')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv7')
reluLayer('Name','relu7')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv8')
reluLayer('Name','relu8')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv9')
reluLayer('Name','relu9')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv10')
reluLayer('Name','relu10')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv11')
reluLayer('Name','relu11')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv12')
reluLayer('Name','relu12')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv13')
reluLayer('Name','relu13')
convolution2dLayer(filterSize,numFilters,'Padding',pad,'Name','conv14')
reluLayer('Name','relu14')
convolution2dLayer(filterSize,D,'Padding',pad,'Name','conv15')
regressionLayer('Name','output')
];
- Specify the training options and train the CNN:
- Use the trained CNN to denoise new NMR data: