  Snapshot compressive imaging (SCI) aims to record three-dimensional signals
via a two-dimensional camera. For the sake of building a fast and accurate SCI
recovery algorithm, we incorporate the interpretability of model-based methods
and the speed of learning-based ones and present a novel dense deep unfolding
network (DUN) with 3D-CNN prior for SCI, where each phase is unrolled from an
iteration of Half-Quadratic Splitting (HQS). To better exploit the
spatial-temporal correlation among frames and address the problem of
information loss between adjacent phases in existing DUNs, we propose to adopt
the 3D-CNN prior in our proximal mapping module and develop a novel dense
feature map (DFM) strategy, respectively. Besides, in order to promote network
robustness, we further propose a dense feature map adaption (DFMA) module to
allow inter-phase information to fuse adaptively. All the parameters are
learned in an end-to-end fashion. Extensive experiments on simulation data and
real data verify the superiority of our method. The source code is available at
https://github.com/jianzhangcs/SCI3D.
