This work proposes a deep learning (DL)-based framework, namely Sim2Real, for
spectral signal reconstruction in reconstructive spectroscopy, focusing on
efficient data sampling and fast inference time. The work focuses on the
challenge of reconstructing real-world spectral signals under the extreme
setting where only device-informed simulated data are available for training.
Such device-informed simulated data are much easier to collect than real-world
data but exhibit large distribution shifts from their real-world counterparts.
To leverage such simulated data effectively, a hierarchical data augmentation
strategy is introduced to mitigate the adverse effects of this domain shift,
and a corresponding neural network for the spectral signal reconstruction with
our augmented data is designed. Experiments using a real dataset measured from
our spectrometer device demonstrate that Sim2Real achieves significant speed-up
during the inference while attaining on-par performance with the
state-of-the-art optimization-based methods.

本研究提出了一种基于深度学习的框架 Sim2Real，用于重建光谱信号中的谱信号重建，在于有效数据采样和快速推理时间。该工作着重解决了仅有经过设备训练的模拟数据的极端设置下，重建真实世界光谱信号的挑战，在此设置中，模拟数据虽然比真实世界数据更容易获取，但存在与真实数据巨大的分布差异。为了有效利用这些模拟数据，引入了分层数据增强策略以减轻域偏移的不利影响，并设计了相应的神经网络来对增强的数据进行光谱信号重建。使用从我们的光谱仪设备测量得到的真实数据集进行的实验证明，Sim2Real 在推理过程中实现了显著的加速，并且在性能方面与最先进的基于优化方法的方法相当。