用多模态扩散模型融合偏振光与低分辨率EBSD数据，实现高效高精度重构
Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data
- 构建无条件多模态扩散模型，联合学习EBSD与偏振光数据的复杂关系
- 仅用25%分辨率的EBSD数据和受损偏振光数据，逼近全分辨率性能
- 适用于材料科学中数据采集慢、噪声大场景，尤其适合实验受限研究者
尽管三维电子背散射衍射（EBSD）显微技术有用，但串行切片的数据采集过程耗时。因此，自然考虑利用偏振光（PL）数据加速EBSD采集，并通过共享信息互补。反之，混沌的PL数据特征也可借助少量EBSD测量得以增强。为内在学习EBSD与PL之间的复杂动态以解决逆问题，我们采用无条件多模态扩散模型，受扩散模型在逆问题中进展的启发。尽管仅在合成数据上训练一次，该模型在真实数据上展现出强泛化能力，可处理低分辨率、噪声、损坏及错配的数据。通过推理时缩放，我们在晶界预测、超分辨率和去噪等多种目标上均取得性能提升。结果显示，仅需25%（1/4分辨率）的EBSD数据和受损的PL数据，即可实现与全分辨率性能几乎无差的结果。
原文摘要 · Abstract (English)
In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-sectioning. Hence, it is natural to look at other modalities, such as polarized light (PL) data, to accelerate EBSD data collection, supplemented with shared information. Complementarily, features in chaotic PL data could even be enriched with a handful of EBSD measurements. To inherently learn the complex dynamics between EBSD and PL to solve these inverse problems, we use an unconditional multimodal diffusion model, motivated by progress in diffusion models for inverse problems. Although trained solely on synthetic data once, our model has strong generalizable capabilities on real data which can be low-resolution, noisy, corrupted, and misregistered. With inference-time scaling, we show gains in performance on a variety of objectives including grain boundary prediction, super-resolution, and denoising. With our model, we demonstrate that there is little difference from full resolution performance with only 25% (1/4 the resolution) of EBSD data and corrupted PL data.
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