ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images
This provides a tool for researchers in cultural heritage or materials science to simulate XRF data from images, but it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of generating MA-XRF raw data from RGB images by developing a Python library that uses a Monte Carlo method to sample XRF signals based on pigment databases, resulting in the release of an open-source tool on PyPi and GitHub.
In this paper we present the first version of ganX -- generate artificially new XRF, a Python library to generate X-ray fluorescence Macro maps (MA-XRF) from a coloured RGB image. To do that, a Monte Carlo method is used, where each MA-XRF pixel signal is sampled out of an XRF signal probability function. Such probability function is computed using a database of couples (pigment characteristic XRF signal, RGB), by a weighted sum of such pigment XRF signal by proximity of the image RGB to the pigment characteristic RGB. The library is released to PyPi and the code is available open source on GitHub.