  The emerging technology of snapshot compressive imaging (SCI) enables
capturing high dimensional (HD) data in an efficient way. It is generally
implemented by two components: an optical encoder that compresses HD signals
into a 2D measurement and an algorithm decoder that retrieves the HD data upon
the hardware-encoded measurement. Over a broad range of SCI applications,
hyperspectral imaging (HSI) and video compressive sensing have received
significant research attention in recent years. Among existing SCI
reconstruction algorithms, deep learning-based methods stand out as their
promising performance and efficient inference. However, the deep reconstruction
network may suffer from overlarge model size and highly-specialized network
design, which inevitably lead to costly training time, high memory usage, and
limited flexibility, thus discouraging the deployments of SCI systems in
practical scenarios. In this paper, we tackle the above challenges by proposing
a simple yet highly efficient reconstruction method, namely stacked residual
network (SRN), by revisiting the residual learning strategy with nested
structures and spatial-invariant property. The proposed SRN empowers
high-fidelity data retrieval with fewer computation operations and negligible
model size compared with existing networks, and also serves as a versatile
backbone applicable for both hyperspectral and video data. Based on the
proposed backbone, we first develop the channel attention enhanced SRN
(CAE-SRN) to explore the spectral inter-dependencies for fine-grained spatial
estimation in HSI. We then employ SRN as a deep denoiser and incorporate it
into a generalized alternating projection (GAP) framework -- resulting in
GAP-SRN -- to handle the video compressive sensing task. Experimental results
demonstrate the state-of-the-art performance, high computational efficiency of
the proposed SRN on two SCI applications.
