  We propose a self-supervised approach for training multi-frame video
denoising networks. These networks predict frame t from a window of frames
around t. Our self-supervised approach benefits from the video temporal
consistency by penalizing a loss between the predicted frame t and a
neighboring target frame, which are aligned using an optical flow. We use the
proposed strategy for online internal learning, where a pre-trained network is
fine-tuned to denoise a new unknown noise type from a single video. After a few
frames, the proposed fine-tuning reaches and sometimes surpasses the
performance of a state-of-the-art network trained with supervision. In
addition, for a wide range of noise types, it can be applied blindly without
knowing the noise distribution. We demonstrate this by showing results on blind
denoising of different synthetic and realistic noises.
