  Shortwave-infrared(SWIR) spectral information, ranging from 1 {\mu}m to
2.5{\mu}m, overcomes the limitations of traditional color cameras in acquiring
scene information. However, conventional SWIR hyperspectral imaging systems
face challenges due to their bulky setups and low acquisition speeds. This work
introduces a snapshot SWIR hyperspectral imaging system based on a metasurface
filter and a corresponding filter selection method to achieve the lowest
correlation coefficient among these filters. This system offers the advantages
of compact size and snapshot imaging. We propose a novel inter and intra prior
learning unfolding framework to achieve high-quality SWIR hyperspectral image
reconstruction, which bridges the gap between prior learning and cross-stage
information interaction. Additionally, We design an adaptive feature transfer
mechanism to adaptively transfer the contextual correlation of multi-scale
encoder features to prevent detailed information loss in the decoder.
Experiment results demonstrate that our method can reconstruct hyperspectral
images with high speed and superior performance over existing methods.
