对抗深度展开网络用于使用最小训练数据的旧大师绘画MA-XRF超分辨率
Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data
AI总结：
本文提出了一种对抗深度展开网络，用于在少量训练数据下提升旧大师绘画MA-XRF的超分辨率效果。
摘要：
高质量的元素分布图能够实现对旧大师绘画材料组成和状况的精确分析。这些图通常是从通过宏X射线荧光（MA-XRF）扫描获取的数据中产生的，这是一种非侵入性技术，收集光谱信息。然而，MA-XRF常常受到获取时间和分辨率之间的权衡限制。实现更高的分辨率需要更长的扫描时间，这在对大型艺术品进行详细分析时可能不切实际。超分辨率MA-XRF提供了一种替代方案，通过提高MA-XRF扫描的质量而减少对长时间扫描会话的需求。本文介绍了一种定制的超分辨率方法，以改进旧大师绘画的MA-XRF分析。我们的方法提出了一种新的对抗神经网络架构用于MA-XRF，灵感来自学习迭代收缩-阈值算法。它专门设计用于无监督方式工作，高效利用有限的可用数据。这种设计避免了需要大量数据集或预训练网络的需求，允许其仅使用单个高分辨率RGB图像和低分辨率MA-XRF数据进行训练。数值结果表明，我们的方法在旧大师绘画的MA-XRF扫描超分辨率方面优于现有的最先进技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
