Apologies if this is a naive or foolish question, but I am trying to learn a bit more about image processing techniques. I had an intuition about Gabor filters but can't seem to find an answer.
If I calculate a bank of Gabor filters for a set of images and reduce them to N features that a machine learning algorithm has determined to be indicative of a specific texture, can these N features be applied to a novel image to "transfer" the texture to the novel image? Perhaps via an inverse Gabor transform? For example, if I have 10 Gabor filters that can accurate classify a texture as "brick", can these 10 filters be applied to a "wood" texture image (picture of a 2x4) to approximate the brick texture on the wood surface?
If possible this is possible, can it be easily implemented in Python?
As far as I understand, this is directly impossible. "When working with Gabor filters, it is common to work with the magnitude response of each filter." https://www.mathworks.com/help/images/texture-segmentation-using-gabor-filters.html That is, information is only about the magnitude of the signal, but there is no information about the phase.