# ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images

arXiv: https://arxiv.org/abs/2304.14078 · HTML: https://mlsanity.com/paper/2304.14078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [On the Origin of Deep Learning](https://mlsanity.com/paper/1702.07800/) (2017)
- [Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction](https://mlsanity.com/paper/1812.10836/) (2018)
- [Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?](https://mlsanity.com/paper/2207.12651/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes](https://mlsanity.com/paper/2410.08826/) (2024)
- [Datacube segmentation via Deep Spectral Clustering](https://mlsanity.com/paper/2401.17695/) (2024)

## Abstract (arXiv)

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.