  Hyperspectral imaging is an essential imaging modality for a wide range of
applications, especially in remote sensing, agriculture, and medicine. Inspired
by existing hyperspectral cameras that are either slow, expensive, or bulky,
reconstructing hyperspectral images (HSIs) from a low-budget snapshot
measurement has drawn wide attention. By mapping a truncated numerical
optimization algorithm into a network with a fixed number of phases, recent
deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI)
have achieved remarkable success. However, DUNs are far from reaching the scope
of industrial applications limited by the lack of cross-phase feature
interaction and adaptive parameter adjustment. In this paper, we propose a
novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network
for SCI, dubbed HerosNet, which includes several phases under the
ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix
and contextually adjust the step size in the gradient descent step, and
hierarchically fuse and interact the hidden states of previous phases to
effectively recover current HSI frames in the proximal mapping step.
Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end
to further improve the reconstruction performance. Finally, our HerosNet is
validated to outperform the state-of-the-art methods on both simulation and
real datasets by large margins. The source code is available at
https://github.com/jianzhangcs/HerosNet.
