A user-friendly platform for deep-learning-based image enhancement in scanning X-ray microscopy.
Authors
Affiliations (2)
Affiliations (2)
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA.
Abstract
Images acquired by scanning X-ray microscopy are often limited in spatial resolution by the finite size and profile of the scanning probe. Here, we present a dedicated graphical user interface for physics-informed image enhancement that incorporates the experimentally characterized probe to decouple probe-induced blurring from the measured images. The software integrates experimentally reconstructed probe intensity into the model training and provides an end-to-end workflow for image super-resolution and deblurring. We demonstrate the approach using differential phase-contrast imaging, where the finite scanning probe limits the recovery of fine spatial features. The enhanced images recover spatial details beyond those directly resolved in the measured data. This platform provides a practical and reproducible tool for physics-informed image enhancement during synchrotron X-ray microscopy experiments.