  A number of deep learning based algorithms have been proposed to recover
high-quality videos from low-quality compressed ones. Among them, some restore
the missing details of each frame via exploring the spatiotemporal information
of neighboring frames. However, these methods usually suffer from a narrow
temporal scope, thus may miss some useful details from some frames outside the
neighboring ones. In this paper, to boost artifact removal, on the one hand, we
propose a Recursive Fusion (RF) module to model the temporal dependency within
a long temporal range. Specifically, RF utilizes both the current reference
frames and the preceding hidden state to conduct better spatiotemporal
compensation. On the other hand, we design an efficient and effective
Deformable Spatiotemporal Attention (DSTA) module such that the model can pay
more effort on restoring the artifact-rich areas like the boundary area of a
moving object. Extensive experiments show that our method outperforms the
existing ones on the MFQE 2.0 dataset in terms of both fidelity and perceptual
effect. Code is available at https://github.com/zhaominyiz/RFDA-PyTorch.
