Motion-Aware Reconstruction for Video Snapshot Compressive Imaging

Video Snapshot Compressive Imaging (SCI) provides an elegant solution for recording fast-dynamics motion through optical compression and computational reconstruction. However, existing video reconstruction algorithms are computationally intensive and typically require substantial computing and storage resources to large areas of static background with minimal information, leading to significant resource waste. To this end, this paper proposes a motion-aware reconstruction paradigm, in which moving objects are separated from the static background to identify Regions Of Interest (ROI) in the measurement domain and then video reconstruction is confined to ROI to save reconstruction costs. Specifically, a Motion Decomposition Network (MoDeNet) is proposed to get ROI by deep unfolding of Half-Quadratic Splitting (HQS). Subsequently, high-performance reconstruction network is used to recover ROI videos. Extensive experiments demonstrate that the proposed paradigm reduces the reconstruction costs of video SCI effectively and efficiently, and the proposed MoDeNet outperforms previous decomposition algorithms in terms of accuracy and speed.