Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging

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://githuacomijianzhangcsISCI3D.

Automated summary

This study introduced a novel dense deep unfolding network (DUN) for snapshot compressive imaging (SCI), combining the interpretability of model-based methods and the speed of learning-based ones, with a 3D-CNN prior for improved spatial-temporal correlation and information fusion speed.