  Long-range temporal alignment is critical yet challenging for video
restoration tasks. Recently, some works attempt to divide the long-range
alignment into several sub-alignments and handle them progressively. Although
this operation is helpful in modeling distant correspondences, error
accumulation is inevitable due to the propagation mechanism. In this work, we
present a novel, generic iterative alignment module which employs a gradual
refinement scheme for sub-alignments, yielding more accurate motion
compensation. To further enhance the alignment accuracy and temporal
consistency, we develop a non-parametric re-weighting method, where the
importance of each neighboring frame is adaptively evaluated in a spatial-wise
way for aggregation. By virtue of the proposed strategies, our model achieves
state-of-the-art performance on multiple benchmarks across a range of video
restoration tasks including video super-resolution, denoising and deblurring.
Our project is available in
\url{https://github.com/redrock303/Revisiting-Temporal-Alignment-for-Video-Restoration.git}.
