Denoising Fast X-Ray Fluorescence Raster Scans of Paintings

TL;DR

This paper introduces a novel denoising method for fast X-ray fluorescence raster scans of paintings, combining dictionary learning and color priors to improve image quality from rapid, noisy data acquisition.

Contribution

It presents a new denoising approach that reduces scan times in XRF imaging of artworks without compromising elemental map quality.

Findings

Effective noise reduction in rapid XRF scans

Preserves elemental distribution accuracy

Reduces imaging time significantly

Abstract

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.