MAP-Inspired Deep Unfolding Network for Distributed Compressive Video Sensing

Abstract

Due to the lack of probabilistic inference guidance, the network structure of current deep learning-based Distributed Compressive Video Sensing (DCVS) algorithms cannot efficiently exploit frame measurements, inter-frame correlations, and intra-frame correlations. In this paper, we infer the optimization equation for DCVS reconstruction according to the maximum a posteriori estimation and propose a Deep Unfolding Multi-Hypothesis Aggregation Network (DUMHAN) to obtain high-quality reconstruction frames. In each stage of the DUMHAN, three modules are designed to implement the three update steps in the inferred optimization equation, which correspond to the three information flows, namely measurement, intra-frame, and inter-frame information flows. In particular, a Multi-Hypothesis Aggregation (MHA) module is developed to enhance the utilization of inter-frame and intra-frame correlation by generating and fusing multiple hypothesis sets. Moreover, we propose the stage-by-stage optical flow update approach in DUMHAN to improve the accuracy of both intra-frame and inter-frame correlation modeling. The experiments demonstrate that the proposed DUMHAN achieves state-of-the-art performance.