  Different from the Single Image Super-Resolution(SISR) task, the key for
Video Super-Resolution(VSR) task is to make full use of complementary
information across frames to reconstruct the high-resolution sequence. Since
images from different frames with diverse motion and scene, accurately aligning
multiple frames and effectively fusing different frames has always been the key
research work of VSR tasks. To utilize rich complementary information of
neighboring frames, in this paper, we propose a multi-stage VSR deep
architecture, dubbed as PP-MSVSR, with local fusion module, auxiliary loss and
re-align module to refine the enhanced result progressively. Specifically, in
order to strengthen the fusion of features across frames in feature
propagation, a local fusion module is designed in stage-1 to perform local
feature fusion before feature propagation. Moreover, we introduce an auxiliary
loss in stage-2 to make the features obtained by the propagation module reserve
more correlated information connected to the HR space, and introduce a re-align
module in stage-3 to make full use of the feature information of the previous
stage. Extensive experiments substantiate that PP-MSVSR achieves a promising
performance of Vid4 datasets, which achieves a PSNR of 28.13dB with only 1.45M
parameters. And the PP-MSVSR-L exceeds all state of the art method on REDS4
datasets with considerable parameters. Code and models will be released in
PaddleGAN\footnote{https://github.com/PaddlePaddle/PaddleGAN.}.
