Convolutional Neural Networks - reasons for changing image data range

Question

When looking at data augmentation techniques for input images in a Convolutional Neural Network, it is often mentioned that you can change/rescale the range of image values from [0,255] to [0,1].

What is the reasoning behind this?

Answer 1

This is scaling (part of preprocessing inputs for any network, not just CNN). Why is it done? This is done to keep the ranges of all the features in the same region. You can refer this answer for more information about the same.
But, here in your case, you only have features regarding the pixel intensities of the image. So, why do you need scaling in this case? This is because most of the parameter initialization, that is being automatically done by the framework you are using, assumes that the data being passed to it is normalized. It tends to make network converge faster, as many researchers have spent time figuring out the right initialization for the network parameters.