  In many applications, including surveillance, entertainment, and restoration,
there is a need to increase both the spatial resolution and the frame rate of a
video sequence. The aim is to improve visual quality, refine details, and
create a more realistic viewing experience. Existing space-time video
super-resolution methods do not effectively use spatio-temporal information. To
address this limitation, we propose a generative adversarial network for joint
space-time video super-resolution. The generative network consists of three
operations: shallow feature extraction, deep feature extraction, and
reconstruction. It uses three-dimensional (3D) convolutions to process temporal
and spatial information simultaneously and includes a novel 3D attention
mechanism to extract the most important channel and spatial information. The
discriminative network uses a two-branch structure to handle details and motion
information, making the generated results more accurate. Experimental results
on the Vid4, Vimeo-90K, and REDS datasets demonstrate the effectiveness of the
proposed method. The source code is publicly available at
https://github.com/FCongRui/3DAttGan.git.
