using noise nmr sequences wavelet coefficient as a cnn-resnet input wish the corresponding nmr sequences wavelet coefficient as the cnn-resnet output to denoise instead forecast in matlab

To use noise NMR sequences wavelet coefficient as a CNN-ResNet input with corresponding NMR sequences wavelet coefficient as the CNN-ResNet output for denoising in MATLAB, you need to follow these steps:

1. Generate a dataset of noisy NMR spectra and the corresponding noise-free spectra (ground truth). You can add different types of noise to the original signal to generate the noisy spectra.
2. Compute the wavelet coefficients for the noisy NMR spectra and the ground truth spectra using the MATLAB wavedec function.
3. Organize the data as input-output pairs and divide the dataset into training, validation, and test sets.
4. Define a CNN-ResNet architecture in MATLAB, such as the one provided in the Deep Learning Toolbox. You can use a series of convolutional and residual layers to learn the mapping from the noisy wavelet coefficients to the denoised wavelet coefficients.
5. Train the CNN-ResNet using the training set and validate using the validation set. Monitor the training loss and validation loss to avoid overfitting.
6. Evaluate the denoising performance on the test set using metrics such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE).
7. Use the trained model to denoise new NMR spectra with unknown noise.

Here is some example code to define a CNN-ResNet architecture in MATLAB:

main.m
inputSize = [32 32 1];
numFilters = 64;

layers = [
    imageInputLayer(inputSize)

    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer

    resnetBlock(numFilters, 1)

    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer

    resnetBlock(numFilters, 2)

    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer

    resnetBlock(numFilters, 3)

    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer

    convolution2dLayer(3,1,'Padding','same')
    regressionLayer
];

function layers = resnetBlock(numFilters, num)
name = ['res' num2str(num) '_'];

layers = [
    convolution2dLayer(3,numFilters,'Padding','same','Name',[name 'conv1'])
    batchNormalizationLayer('Name',[name 'bn1'])
    reluLayer('Name',[name 'relu1'])

    convolution2dLayer(3,numFilters,'Padding','same','Name',[name 'conv2'])
    batchNormalizationLayer('Name',[name 'bn2'])
    reluLayer('Name',[name 'relu2'])

    additionLayer(2,'Name',[name 'add'])
    reluLayer('Name',[name 'relu'])
];
end
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