Collaborative Feedback Discriminative Propagation for Video
  Super-Resolution
Hao Li Xiang Chen Jiangxin Dong Jinhui Tang Jinshan Pan
The key success of existing video super-resolution (VSR) methods stems mainly
from exploring spatial and temporal information, which is usually achieved by a
recurrent propagation module with an alignment module. However, inaccurate
alignment usually leads to aligned features with significant artifacts, which
will be accumulated during propagation and thus affect video restoration.
Moreover, propagation modules only propagate the same timestep features forward
or backward that may fail in case of complex motion or occlusion, limiting
their performance for high-quality frame restoration. To address these issues,
we propose a collaborative feedback discriminative (CFD) method to correct
inaccurate aligned features and model long -range spatial and temporal
information for better video reconstruction. In detail, we develop a
discriminative alignment correction (DAC) method to adaptively explore
information and reduce the influences of the artifacts caused by inaccurate
alignment. Then, we propose a collaborative feedback propagation (CFP) module
that employs feedback and gating mechanisms to better explore spatial and
temporal information of different timestep features from forward and backward
propagation simultaneously. Finally, we embed the proposed DAC and CFP into
commonly used VSR networks to verify the effectiveness of our method.
Quantitative and qualitative experiments on several benchmarks demonstrate that
our method can improve the performance of existing VSR models while maintaining
a lower model complexity. The source code and pre-trained models will be
available at \url{https://github.com/House-Leo/CFDVSR}.