FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring
Authors: Geunhyuk Youk, Jihyong Oh, Munchurl Kim

We present a joint learning scheme of video super-resolution and deblurring called VSRDB to restore clean high-resolution (HR) videos from blurry low-resolution (LR) ones. This joint restoration problem has drawn much less attention compared to single restoration problems. In this paper we propose a novel flow-guided dynamic filtering (FGDF) and iterative feature refinement with multi-attention (FRMA) which constitutes our VSRDB framework denoted as FMA-Net. Specifically our proposed FGDF enables precise estimation of both spatio-temporally-variant degradation and restoration kernels that are aware of motion trajectories through sophisticated motion representation learning. Compared to conventional dynamic filtering the FGDF enables the FMA-Net to effectively handle large motions into the VSRDB. Additionally the stacked FRMA blocks trained with our novel temporal anchor (TA) loss which temporally anchors and sharpens features refine features in a coarse-to-fine manner through iterative updates. Extensive experiments demonstrate the superiority of the proposed FMA-Net over state-of-the-art methods in terms of both quantitative and qualitative quality. Codes and pre-trained models are available at: https://kaist-viclab.github.io/fmanet-site.