  The demand for high-resolution videos has been consistently rising across
various domains, propelled by continuous advancements in science, technology,
and societal. Nonetheless, challenges arising from limitations in imaging
equipment capabilities, imaging conditions, as well as economic and temporal
factors often result in obtaining low-resolution images in particular
situations. Space-time video super-resolution aims to enhance the spatial and
temporal resolutions of low-resolution and low-frame-rate videos. The currently
available space-time video super-resolution methods often fail to fully exploit
the abundant information existing within the spatio-temporal domain. To address
this problem, we tackle the issue by conceptualizing the input low-resolution
video as a cuboid structure. Drawing on this perspective, we introduce an
innovative methodology called "Cuboid-Net," which incorporates a multi-branch
convolutional neural network. Cuboid-Net is designed to collectively enhance
the spatial and temporal resolutions of videos, enabling the extraction of rich
and meaningful information across both spatial and temporal dimensions.
Specifically, we take the input video as a cuboid to generate different
directional slices as input for different branches of the network. The proposed
network contains four modules, i.e., a multi-branch-based hybrid feature
extraction (MBFE) module, a multi-branch-based reconstruction (MBR) module, a
first stage quality enhancement (QE) module, and a second stage cross frame
quality enhancement (CFQE) module for interpolated frames only. Experimental
results demonstrate that the proposed method is not only effective for spatial
and temporal super-resolution of video but also for spatial and angular
super-resolution of light field.
