Effect of normalization on activation

I know that normalization tends to center the data making the learning process more stable.
But what is it’s effect on the intermediate activation/feature maps.
for example below is an image before normalization along with it’s histogram

histogram of image
Same image after histogram equalization is done along with it’s histogram

image histogram after equalization is done
clearly we can see that after the equalization is done,the image looks more dynamic,where you can clearly see the buildings.

so i am thinking that if normalization wasn’t done,then different parts in the image would end up firing differently,thereby making it difficult for it to learn features and discriminate between them. am i right in thinking like this?