yeah, this is the really relevant bit:

Spatial scanning imaging spectrometers are typically limited in their imaging speed due to the use of mechanical scanning, allowing for HSI mostly in stationary scenes. If there is a relative displacement between the spatial scanning imaging spectrometer and the object in the imaging process, adverse effects such as artifacts and stretching will appear in the acquired images. We found that these effects arise from the blending of scene information from multiple moments within one measurement (Fig. 1C). Each spectrum in the scanned HSI measurement corresponds to a specific acquisition moment. We can reconstruct a complete HSI video of all acquisition moments by establishing the inverse process of the acquisition. Assuming that no variation occurs in the spectral properties of the scene materials or the illumination during the imaging process, this inverse process is to find the spatial positions corresponding to all spectra in the scanned HSI measurement at all moments. Therefore, the reconstruction requires the incorporation of scene motion information, which can be achieved by introducing a common video-rate RGB camera aligned with the scanning-based imaging spectrometer (Fig. 1A, B).

hyperspectral imaging isn’t new, the breakthrough is in combining it with motion video in a way that the information you’re getting with the spectral scan gets assigned correctly to objects within a moving scene, which is really interesting. sounds like it partly depends on new hardware:

https://english.bit.edu.cn/2024-11/29/c_1049353.htm

The team has overcome multiple bottlenecks and created an on-chip spectral multiplexing sensing architecture, which subverts traditional geometric beam splitting architecture, narrowband measurement mechanism, and physical measurement output mode. It has achieved computational spectral imaging with on-chip broadband differentiation control, completed the difficult process from blank to formed, and opened up new fields of on-chip spectral research.