  In video denoising, the adjacent frames often provide very useful
information, but accurate alignment is needed before such information can be
harnassed. In this work, we present a multi-alignment network, which generates
multiple flow proposals followed by attention-based averaging. It serves to
mimic the non-local mechanism, suppressing noise by averaging multiple
observations. Our approach can be applied to various state-of-the-art models
that are based on flow estimation. Experiments on a large-scale video dataset
demonstrate that our method improves the denoising baseline model by 0.2dB, and
further reduces the parameters by 47% with model distillation. Code is
available at https://github.com/IndigoPurple/MANet.
