Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data

TL;DR

This paper presents an adversarial deep-unfolding neural network for super-resolution of MA-XRF scans of Old Master paintings, effectively enhancing resolution with minimal training data and outperforming existing methods.

Contribution

It introduces a novel unsupervised adversarial deep-unfolding network inspired by iterative algorithms, designed specifically for MA-XRF super-resolution with limited training data.

Findings

Outperforms state-of-the-art super-resolution methods on MA-XRF data

Operates effectively with only a single RGB image and low-res MA-XRF data

Demonstrates improved resolution and material analysis accuracy

Abstract

High-quality element distribution maps enable precise analysis of the material composition and condition of Old Master paintings. These maps are typically produced from data acquired through Macro X-ray fluorescence (MA-XRF) scanning, a non-invasive technique that collects spectral information. However, MA-XRF is often limited by a trade-off between acquisition time and resolution. Achieving higher resolution requires longer scanning times, which can be impractical for detailed analysis of large artworks. Super-resolution MA-XRF provides an alternative solution by enhancing the quality of MA-XRF scans while reducing the need for extended scanning sessions. This paper introduces a tailored super-resolution approach to improve MA-XRF analysis of Old Master paintings. Our method proposes a novel adversarial neural network architecture for MA-XRF, inspired by the Learned Iterative Shrinkage-Thresholding Algorithm. It is specifically designed to work in an unsupervised manner, making efficient use of the limited available data. This design avoids the need for extensive datasets or pre-trained networks, allowing it to be trained using just a single high-resolution RGB image alongside low-resolution MA-XRF data. Numerical results demonstrate that our method outperforms existing state-of-the-art super-resolution techniques for MA-XRF scans of Old Master paintings.