2000 set of nmr sequences contain noise not image to wavelet coefficient as a cnn input and the corresponding clean nmr sequences wavelet coefficient as the output in matlab

main.m
% read your NMR noisy datasets
nmr_noisy_data = load('nmr_noisy_data.mat');
noisy_data = nmr_noisy_data.nmr_data; % assuming data in nmr_data key


% define parameters for wavelet decomposition
wname = 'db6'; % use Daubechies wavelet
levels = 3; % perform 3-level wavelet decomposition
% wavelet decomposition for each dataset
[n, m] = size(noisy_data); % number of sequences, and length of each sequence
coefficients_mat = zeros(n, levels*m); % matrix to hold coefficients
for i=1:n
   % perform 1-D wavelet decomposition
   [C, L] = wavedec(noisy_data(i,:),levels,wname);
   % concatenate all coefficients in a single row
   Coefficients_row = C(1:L(1));
   for j=2:levels+1
       Coefficients_row = [Coefficients_row C(L(j-1)+1:L(j))];
   end
   coefficients_mat(i,:) = Coefficients_row;
end

% store coefficients as input for CNN
save('input_coeffs.mat', 'coefficients_mat');
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main.m
% read your NMR clean datasets
nmr_clean_data = load('nmr_clean_data.mat');
clean_data = nmr_clean_data.nmr_data; % assuming data in nmr_data key

% define parameters for wavelet decomposition
wname = 'db6'; % use Daubechies wavelet
levels = 3; % perform 3-level wavelet decomposition
% wavelet decomposition for each dataset
[n, m] = size(clean_data); % number of sequences, and length of each sequence
coefficients_mat = zeros(n, levels*m); % matrix to hold coefficients
for i=1:n
   % perform 1-D wavelet decomposition
   [C, L] = wavedec(clean_data(i,:),levels,wname);
   % concatenate all coefficients in a single row
   Coefficients_row = C(1:L(1));
   for j=2:levels+1
       Coefficients_row = [Coefficients_row C(L(j-1)+1:L(j))];
   end
   coefficients_mat(i,:) = Coefficients_row;
end

% store coefficients as output for CNN
save('output_coeffs.mat', 'coefficients_mat');
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