Say, for instance, if I had image data from one high resolution digital camera and wanted to make it look like it was taken from another, lower resolution, digital camera? Would training input/output pairs of overlapping images be a good way to do this? What is this technique called?
For example, say I wanted to be able to count benches in parks in LOW resolution imagery. Could I go through these sample images and create an appropriate dataset of high and low resolution pairs to train a network to learn what a low resolution bench looked like? Would I be able to discern low resolution benches if my training set was incredibly diverse (image chips if entire city parks vs individual objects like fountains, trees and statues)?
I like this example because the images come from different sensors as well as being different resolutions. Some of my research has led me to super resolution, which is kind of the opposite of what I'm trying to do.
As for the amount of data, it would be painstaking but not technically difficult to get overlapping high and low resolution imagery.