Deep Tensor ADMM-Net for Snapshot Compressive Imaging

Abstract

Snapshot compressive imaging (SCI) systems have been developed to capture high-dimensional ($\ge$ 3) signals using low-dimensional off-the-shelf sensors, \ie, mapping multiple video frames into a single measurement frame. One key module of a SCI system is an accurate decoder that recovers the original video frames. However, existing model-based decoding algorithms require exhaustive parameter tuning with prior knowledge and cannot support practical applications due to the extremely long running time. In this paper, we propose a deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds. Firstly, we start with a standard tensor ADMM algorithm, unfold its inference iterations into a layer-wise structure, and design a deep neural network based on tensor operations. Secondly, instead of relying on a pre-specified sparse representation domain, the network learns the domain of low-rank tensor through stochastic gradient descent. It is worth noting that the proposed deep tensor ADMM-Net has potentially mathematical interpretations. On public video data, the simulation results show the proposed {method} achieves average $0.8 \sim 2.5$ dB improvement in PSNR and $0.07 \sim 0.1$ in SSIM, and $1500\times \sim 3600 \times$ speedups over the state-of-the-art methods. On real data captured by SCI cameras, the experimental results show comparable visual results with the state-of-the-art methods but in much shorter running time.