  Video denoising aims at removing noise from videos to recover clean ones.
Some existing works show that optical flow can help the denoising by exploiting
the additional spatial-temporal clues from nearby frames. However, the flow
estimation itself is also sensitive to noise, and can be unusable under large
noise levels. To this end, we propose a new multi-scale refined optical
flow-guided video denoising method, which is more robust to different noise
levels. Our method mainly consists of a denoising-oriented flow refinement
(DFR) module and a flow-guided mutual denoising propagation (FMDP) module.
Unlike previous works that directly use off-the-shelf flow solutions, DFR first
learns robust multi-scale optical flows, and FMDP makes use of the flow
guidance by progressively introducing and refining more flow information from
low resolution to high resolution. Together with real noise degradation
synthesis, the proposed multi-scale flow-guided denoising network achieves
state-of-the-art performance on both synthetic Gaussian denoising and real
video denoising. The codes will be made publicly available.
