Online video super-resolution (online-VSR) highly relies on an effective
alignment module to aggregate temporal information, while the strict latency
requirement makes accurate and efficient alignment very challenging. Though
much progress has been achieved, most of the existing online-VSR methods
estimate the motion fields of each frame separately to perform alignment, which
is computationally redundant and ignores the fact that the motion fields of
adjacent frames are correlated. In this work, we propose an efficient Temporal
Motion Propagation (TMP) method, which leverages the continuity of motion field
to achieve fast pixel-level alignment among consecutive frames. Specifically,
we first propagate the offsets from previous frames to the current frame, and
then refine them in the neighborhood, which significantly reduces the matching
space and speeds up the offset estimation process. Furthermore, to enhance the
robustness of alignment, we perform spatial-wise weighting on the warped
features, where the positions with more precise offsets are assigned higher
importance. Experiments on benchmark datasets demonstrate that the proposed TMP
method achieves leading online-VSR accuracy as well as inference speed. The
source code of TMP can be found at
\href{https://github.com/xtudbxk/TMP}{this https URL}.

提出了一种高效的时间运动传播（TMP）方法，利用运动场的连续性实现连续帧之间的快速像素级对齐，通过先前帧的偏移向当前帧传播，并在邻域内进行进一步调整，同时对扭曲特征进行空间加权，以增强对齐的鲁棒性。