Assuming I have two images, apple and orange; also assuming a filter kernel that transforms an apple image into an orange image possibly exists, how would some series of Fourier Transformations (and other spectral operations) get me a filter kernel? Is this possible? If possible, can it be immune to rotations/scaling/translation in spatial domain? (apple vs rotated apple)
In another way, if orange is
IFT(FT(apple) * FT(filter))
then how can filter be found using only apple and orange? If it is something like
filter = IFT(FT(apple) @@ FT(orange))
then what could
@@ be? Is this possible?
Side question: if this is possible, are we able to extract "similarness" of an apple and an orange, just by looking at the result kernel form
@@ operation? I mean, if kernel has only 1 on center and 0 on all other parts, this would be totally equal (both are apple or both are oranges) but what about other cases? Something like root mean squares of all kernel points?