用神经网络从少量数据重建高维光谱图像，实验时间缩短32倍。
Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling
- 用MLP建模稀疏采样点与光谱强度关系，实现4D光谱重建。
- 仅需1/32采样量即可高保真还原振荡与非振荡动态。
- 自适应采样策略可边实验边优化采样点，适合快速化学成像。
高维超光谱成像（HSI）能够可视化超快分子动力学和复杂的异质光谱。然而，在二维红外（2DIR）光谱学——一种相干多维光谱技术（CMDS）——中解析空间变化的振动耦合，需要极长的数据采集时间，主要受密集奈奎斯特采样需求和大量信号积累的制约。为此，我们提出一种物理信息神经表示方法，可从稀疏实验测量中高效重建密集的空间分辨2DIR超光谱图像。具体而言，使用多层感知机（MLP）建模子采样4D坐标与其对应光谱强度之间的关系，并从有限观测中恢复密集采样的4D光谱。重建结果表明，该方法仅需少量样本即可忠实还原实验测量中的振荡与非振荡光谱动态。此外，我们开发了一种损失感知自适应采样方法，在实验过程中逐步引入潜在信息量高的样本进行迭代采集。实验结果表明，所提方法仅需1/32的采样预算即可实现高保真光谱恢复，相较全采样有效将总实验时间缩短达32倍。该框架为任何具有超立方体数据的实验（如多维光谱与超光谱成像）提供了可扩展的加速方案，为瞬态生物与材料系统的快速化学成像铺平道路。
原文摘要 · Abstract (English)
High-dimensional hyperspectral imaging (HSI) enables the visualization of ultrafast molecular dynamics and complex, heterogeneous spectra. However, applying this capability to resolve spatially varying vibrational couplings in two-dimensional infrared (2DIR) spectroscopy, a type of coherent multidimensional spectroscopy (CMDS), necessitates prohibitively long data acquisition, driven by dense Nyquist sampling requirements and the need for extensive signal accumulation. To address this challenge, we introduce a physics-informed neural representation approach that efficiently reconstructs dense spatially-resolved 2DIR hyperspectral images from sparse experimental measurements. In particular, we used a multilayer perceptron (MLP) to model the relationship between the sub-sampled 4D coordinates and their corresponding spectral intensities, and recover densely sampled 4D spectra from limited observations. The reconstruction results demonstrate that our method, using a fraction of the samples, faithfully recovers both oscillatory and non-oscillatory spectral dynamics in experimental measurement. Moreover, we develop a loss-aware adaptive sampling method to progressively introduce potentially informative samples for iterative data collection while conducting experiments. Experimental results show that the proposed approach achieves high-fidelity spectral recovery using only $1/32$ of the sampling budget, as opposed to exhaustive sampling, effectively reducing total experiment time by up to 32-fold. This framework offers a scalable solution for accelerating any experiments with hypercube data, including multidimensional spectroscopy and hyperspectral imaging, paving the way for rapid chemical imaging of transient biological and material systems.
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