# Denoising Fast X-Ray Fluorescence Raster Scans of Paintings

arXiv: https://arxiv.org/abs/2206.01740 · HTML: https://mlsanity.com/paper/2206.01740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction](https://mlsanity.com/paper/1812.10836/) (2018)

## Abstract (arXiv)

Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.