Yes, same "real" number of images (unique signal), but through augmentation you can improve generalization while at the same time reducing the need for a larger dataset.
this work especially well if you know something about the types of noise/augmentations that will naturally be in your problem domain and can simulate them artificially to encourage the network to learn to disregard them.
of course this can be pushed only so far. if consider the case where you have 1,000 unique images with no augmentation, versus a single image with 1,000 types of augmentation, you can imagine that in the first case the system will generalize far better than the second case.
cheers,
andrew