  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 course-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
