  Denoisers trained with synthetic data often fail to cope with the diversity
of unknown noises, giving way to methods that can adapt to existing noise
without knowing its ground truth. Previous image-based method leads to noise
overfitting if directly applied to video denoisers, and has inadequate temporal
information management especially in terms of occlusion and lighting variation,
which considerably hinders its denoising performance. In this paper, we propose
a general framework for video denoising networks that successfully addresses
these challenges. A novel twin sampler assembles training data by decoupling
inputs from targets without altering semantics, which not only effectively
solves the noise overfitting problem, but also generates better occlusion masks
efficiently by checking optical flow consistency. An online denoising scheme
and a warping loss regularizer are employed for better temporal alignment.
Lighting variation is quantified based on the local similarity of aligned
frames. Our method consistently outperforms the prior art by 0.6-3.2dB PSNR on
multiple noises, datasets and network architectures. State-of-the-art results
on reducing model-blind video noises are achieved. Extensive ablation studies
are conducted to demonstrate the significance of each technical components.
