用XRF数据重建古画色彩，解决小样本与计算效率问题
Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes
- 构建合成XRF数据集，缓解真实数据少的难题
- 提出变分嵌入网络，压缩数据并提升聚类效率
- 实现从XRF光谱到彩色图像的精准映射，适合艺术修复研究
本文提出并验证了一种基于原始X射线荧光（XRF）分析数据的虚拟绘画着色重建流程。由于真实数据集规模小，我们从现有XRF光谱数据库生成合成数据；为提升模型泛化能力并解决内存占用和推理时间问题，设计了深度变分嵌入网络，将XRF光谱映射到低维、适合K均值聚类的度量空间。随后训练多组模型，实现对嵌入后XRF图像的颜色图像映射。实验报告了该流程在视觉质量指标上的表现，并讨论了结果的可行性与局限性。
原文摘要 · Abstract (English)
In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results.
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