a data.mat as the resnet18' input and a clean data.mat as its output in matlab

Assuming you have a data.mat file containing the input data, and a clean_data.mat file containing the ground-truth clean data corresponding to the input data, you can use the ResNet-18 network in MATLAB to train the model as follows:

main.m
% Load the training data
load('data.mat'); % Assuming that `data.mat` contains the input data
load('clean_data.mat'); % Assuming that `clean_data.mat` contains the output data

% Specify the network architecture
net = resnet18;

% Specify the training options
opts = trainingOptions('sgdm', ...
    'InitialLearnRate',0.001, ...
    'MaxEpochs',10, ...
    'MiniBatchSize',64, ...
    'Shuffle','every-epoch', ...
    'CheckpointPath', tempdir);

% Train the network
[net, info] = trainNetwork(data, clean_data, net, opts);
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In this code snippet, we first load the data.mat and clean_data.mat files using the load function. Then, we create an instance of the ResNet-18 network using the resnet18 function. Next, we specify the training options using the trainingOptions function. Finally, we train the network using the trainNetwork function.

After training, you can use the trained network to predict the output for a new test image by passing the image through the network. For example, assuming you have a test image saved as test_image.mat, you can use the following code snippet to predict its output using the trained network:

main.m
% Load the test image
load('test_image.mat');

% Pass the test image through the network to obtain the predicted output
predicted_output = predict(net, test_image);
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Remember to replace test_image.mat with the actual filename of your test image.