# Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?

arXiv: https://arxiv.org/abs/2207.12651 · HTML: https://mlsanity.com/paper/2207.12651/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images](https://mlsanity.com/paper/2304.14078/) (2023)
- [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)
- [XRFormer: Multiscale Tokenization for XRF Representation Learning](https://mlsanity.com/paper/2607.06424/) (2026)

## Abstract (arXiv)

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping by expert interpretations of measured spectra. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging for automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pixel-wise pigment identification based on XRF imaging remains an obstacle due to the high noise level compared with averaged spectra. Therefore, we developed a deep-learning-based end-to-end pigment identification framework to fully automate the pigment identification process. In particular, it offers high sensitivity to the underlying pigments and to the pigments with a low concentration, therefore enabling satisfying results in mapping the pigments based on single-pixel XRF spectrum. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Po`emes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.