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

TL;DR

This paper introduces a dense deep unfolding network with 3D-CNN prior for snapshot compressive imaging, combining interpretability, speed, and spatial-temporal correlation exploitation for improved 3D signal recovery.

Contribution

It proposes a novel dense deep unfolding network with 3D-CNN prior, dense feature map strategy, and feature map adaption modules for enhanced SCI reconstruction.

Findings

Outperforms existing methods on simulation data

Effective in real data experiments

Demonstrates robustness and high accuracy

Abstract

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.