Order of normalization / augmentation for image classification I'm currently working on a common image classification with CNN.

I would like to use both normalization (substract mean / divide by std per channel) and data augmentation (rotation, color, blur, ...) but I don't know how to use them together. 

Which order should I use ? 


First normalize with parameters based only on original images and then augment it (augment a normalized image is relevant ? should I ban some type of augmentation like color ?)
Augment data and apply normalization based on all image (compute mean/ std with augmented images) which seems to be counterintuitive.
Augment data and apply normalization based on only original image which means that data are not really normalized
Or don't use both methods
You can use both methods, in the vast majority of examples I have seen and worked with the usual order is.  


Augmentate your data, rotations, zoomings, flips....
Normalize your images based on the new and augmented dataset.


Doing it like this you can also see the results of the augmentation phase, while if you normalize it first you cannot visualize it