Interpolation is a common way to make an image fit the right input shape for a neural network.
But is there any point in using interpolation to make it easier for the network to learn?
I assume interpolation adds no extra information to the input; It only uses existing information to increase the resolution and fill missing values.
However, sometimes I have observed that while I can not see anything with my human eye, using some kind of advanced interpolation technique such as b-spline interpolation makes it crystal clear that the object i am looking for is in the image, especially in the domain of infrared images.
So, is there any benefit for using interpolation rather than feeding a low dimensional image to a neural network?