  Video denoising is to remove noise from noise-corrupted data, thus recovering
true signals via spatiotemporal processing. Existing approaches for
spatiotemporal video denoising tend to suffer from motion blur artifacts, that
is, the boundary of a moving object tends to appear blurry especially when the
object undergoes a fast motion, causing optical flow calculation to break down.
In this paper, we address this challenge by designing a first-image-then-video
two-stage denoising neural network, consisting of an image denoising module for
spatially reducing intra-frame noise followed by a regular spatiotemporal video
denoising module. The intuition is simple yet powerful and effective: the first
stage of image denoising effectively reduces the noise level and, therefore,
allows the second stage of spatiotemporal denoising for better modeling and
learning everywhere, including along the moving object boundaries. This
two-stage network, when trained in an end-to-end fashion, yields the
state-of-the-art performances on the video denoising benchmark Vimeo90K dataset
in terms of both denoising quality and computation. It also enables an
unsupervised approach that achieves comparable performance to existing
supervised approaches.
