  Coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages
over traditional methods in dynamically acquiring Hyper-Spectral Image (HSI),
but there are the following problems. 1) Traditional mask relies on random
patterns or analytical design, both of which limit the performance improvement
of CASSI. 2) Existing high-quality reconstruction algorithms are slow in
reconstruction and can only reconstruct scene information offline. To address
the above two problems, this paper designs the AMDC-CASSI system, introducing
RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the
reconstruction quality. The existing SOTA reconstruction schemes are based on
transformer, but the operation of self-attention pulls down the operation
efficiency of the network. In order to improve the inference speed of the
reconstruction network, this paper proposes An MLP Architecture for
Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure
of the network. Numerous experiments have shown that MLP performs no less well
than transformer-based structures for HSI reconstruction, while MLP greatly
improves the network inference speed and has less number of parameters and
operations, our method has a 8 db improvement over SOTA and at least a 5-fold
improvement in reconstruction speed. (https://github.com/caizeyu1992/MLP-AMDC.)
