A Lightweight Recurrent Grouping Attention Network for Video
  Super-Resolution
Yonggui Zhu Guofang Li
Effective aggregation of temporal information of consecutive frames is the
core of achieving video super-resolution. Many scholars have utilized
structures such as sliding windows and recurrent to gather spatio-temporal
information of frames. However, although the performance of the constructed VSR
models is improving, the size of the models is also increasing, exacerbating
the demand on the equipment. Thus, to reduce the stress on the device, we
propose a novel lightweight recurrent grouping attention network. The
parameters of this model are only 0.878M, which is much lower than the current
mainstream model for studying video super-resolution. We design forward feature
extraction module and backward feature extraction module to collect temporal
information between consecutive frames from two directions. Moreover, a new
grouping mechanism is proposed to efficiently collect spatio-temporal
information of the reference frame and its neighboring frames. The attention
supplementation module is presented to further enhance the information
gathering range of the model. The feature reconstruction module aims to
aggregate information from different directions to reconstruct high-resolution
features. Experiments demonstrate that our model achieves state-of-the-art
performance on multiple datasets.