Could you explain, what this method is doing, as the docs don’t seem to give much information:
Linearly scales each image in `image` to have mean 0 and variance 1.
For each 3-D image `x` in `image` , computes `(x - mean) / adjusted_stddev` , where
* `mean` is the average of all values in `x`
* `adjusted_stddev = max(stddev, 1.0/sqrt(N))` is capped away from 0 to protect against division by 0 when handling uniform images
* `N` is the number of elements in `x`
* `stddev` is the standard deviation of all values in `x`
I don’t understand what the “per image” part of this normalization is, if the mean is the "average of all values in x and the stddev also seems to use all elements.