Denoising Fast X-Ray Fluorescence Raster Scans of Paintings
This work addresses a domain-specific issue for cultural heritage analysis by enabling faster non-invasive imaging, though it appears incremental as it builds on existing dictionary learning techniques.
The paper tackled the slow acquisition problem in macro X-ray fluorescence imaging of cultural heritage objects by proposing a method to restore noisy, rapidly acquired data, achieving reduced scan times without sacrificing elemental map and XRF volume quality.
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.