I am training a neural network on a dataset of images. I am using the augmentedImageDatastore object during training to have some basic random geometric transformations that change at every epoch :
augmenter = imageDataAugmenter( ...
'RandXTranslation',[-10 10], ...
'RandYTranslation',[-10 10])
imageSize = [128 128 3];
auimds = augmentedImageDatastore(imageSize,XData,YData,'DataAugmentation',augmenter)
layers = ...
options = ...
NET = trainNetwork(auimds,layers,options);
I know that there exists an denoisingImageDatastore that has the same functionality but for noise instead of geometric transformations, but I can't find a way to combine both functionnalities during training.
My current approach is to add noise once to my images and then using these noisy images as XData in the augmentedImageDatastore, but it means that the noise will always be the same for a given image. I would like to have a random translation and random noise added to my images, while keeping the fact that the noise and the translations are randomly changed at each epoch.
Is it possible to do that?
