It is just like the code you have provided. You use this custom dataset instead of previously constructed one and pass it to the Dataloader. Then in a loop, get batches of data which in this case would be two batch of input and noisy images.
train_dataset = Dataset(path_inputs='path_to_original_images',
                              img_dir='path_to_noisy_images/')

train_loader = DataLoader(dataset=train_dataset,
                          batch_size=128,
                          shuffle=True,
                          num_workers=4)

for origin, noisy in data_loader:
    x = origin  # [batch, channel, height, width]
    y = noisy  # [batch, channel, height, width]
    ## do your stuff

Note that I did not provided the code of loading images from paths or even how to get the path, that is actually more python rather than pytorch. You can use glob and PIL or ‘Open CV’ for loading images.