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Video denoising aims at removing noise from videos to recover clean ones.Some existing works show that optical flow can help the denoising by exploitingthe additional spatial-temporal clues from nearby frames. However, the flowestimation itself is also sensitive to noise, and can be unusable under largenoise levels. To this end, we propose a new multi-scale refined opticalflow-guided video denoising method, which is more robust to different noiselevels. 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 firstlearns robust multi-scale optical flows, and FMDP makes use of the flowguidance by progressively introducing and refining more flow information fromlow resolution to high resolution. Together with real noise degradationsynthesis, the proposed multi-scale flow-guided denoising network achievesstate-of-the-art performance on both synthetic Gaussian denoising and realvideo denoising. The codes will be made publicly available.

Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising