  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.
