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

FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring