Deep network-based image and video Compressive Sensing(CS) has attracted
increasing attentions in recent years. However, in the existing deep
network-based CS methods, a simple stacked convolutional network is usually
adopted, which not only weakens the perception of rich contextual prior
knowledge, but also limits the exploration of the correlations between temporal
video frames. In this paper, we propose a novel Hierarchical InTeractive Video
CS Reconstruction Network(HIT-VCSNet), which can cooperatively exploit the deep
priors in both spatial and temporal domains to improve the reconstruction
quality. Specifically, in the spatial domain, a novel hierarchical structure is
designed, which can hierarchically extract deep features from keyframes and
non-keyframes. In the temporal domain, a novel hierarchical interaction
mechanism is proposed, which can cooperatively learn the correlations among
different frames in the multiscale space. Extensive experiments manifest that
the proposed HIT-VCSNet outperforms the existing state-of-the-art video and
image CS methods in a large margin.

本文提出了一种新的 Hierarchical InTeractive Video CS Reconstruction Network（HIT-VCSNet）深度网络图像和视频压缩感知方法，通过空间和时间领域中的深度先验共同提取上下文信息，并在不同尺度空间中协同地学习不同帧之间的相关性，在图像和视频压缩感知领域中表现出卓越性能.