  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.
