REPVSR: Efficient Video Super-Resolution via Structural Re-Parameterization

KunLei Hu, Dahai Yu

Abstract: Recent advances in video super-resolution (VSR) explored the power of deep learning to achieve a better reconstruction performance. However, the high computational cost still hinders it from practical usage that demands real-time performance (24 fps). In this paper, we propose a re-parameterization video superresolution(REPVSR) to accelerate the reconstruction speed with efficient and generic network. Specifically, we propose re-parameterizable building blocks, namely Super-Resolution Multi-Branch block (SRMB) for efficient SR part design and FlowNet Multi-Branch block (FNMB) for optical flow estimation part. The blocks extract features in multiple paths in the training stage, and merge the multiple operations into one single 3×3 convolution in the inference stage. We then propose an extremely efficient VSR network based on SRMB and FNMB, namely REPVSR. Extensive experiments demonstrate the effectiveness and efficiency of REPVSR.

External IDs:dblp:conf/visigrapp/HuY25