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: this https URL

通过联合学习视频超分辨率和去模糊的方法，本文提出了一种名为 VSRDB 的视频清晰恢复方案，以从模糊的低分辨率视频中还原出干净的高分辨率视频。通过 FGDF 和 FRMA 两个部分构成的 FMA-Net 框架，我们的方法能够精确估计时空变化的退化和恢复内核，并通过迭代特征细化的方式进行多注意力的特征改进。鲁棒的实验证明了本文提出的 FMA-Net 在定量和定性方面优于现有的方法。