Bidirectional recurrent deformable alignment network for video super-resolution

The challenge in video super-resolution(VSR) is maximizing temporal correlation in video sequences and achieving precise inter-frame alignment. We proposed a bidirectional recurrent network integrating multi-scale deformable alignment with temporal attention fusion. We first designed a multi-scale cascading deformable alignment module, which captures spatiotemporal information at different scales through gradual refinement. It employs deformable convolutions for adaptive feature alignment to address complex motion and deformation issues. To effectively fuse the temporal information, we have added a temporal attention fusion module. This module dynamically weights the information of each frame using an attention mechanism. We carried out a comprehensive series of experiments on multiple standard VSR datasets. The outcomes indicate that our method achieved significant performance.