In the end, when you normalize you are getting zero mean and unitary variance.
This is roughly a circle in a plot.
Suppose you have an image big (and variate) enough. If you sample pixels from that image and plot them, you would end up filling the whole circle, namely, having points for all the possible situations.
As you image-wise normalized , each image follows same distribution, 0 mean unitary variance. If you sample pixels from different images and you plot them together you would get 2nd plot. All the points fall in same range.
On contrary, if you dataset-wise normalize, each image would have a different distribution with a unique mean and unique variance. If you sample pixels from each image, you would get different distributions like plot 3.
Explaining it the other way around.
If you apply per-image normalization and then get a single image. You would observe mean of its pixels is zero, and std 1.
If you apply dataset normalization and then you get a single image you would observe mean of its pixels is not zero and std is not one.