  To facilitate video denoising research, we construct a compelling dataset,
namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean
dynamic video pairs in both sRGB and RAW format. Compared with existing
datasets consisting of limited motion information, PVDD covers dynamic scenes
with varying and natural motion. Different from datasets using primarily
Gaussian or Poisson distributions to synthesize noise in the sRGB domain, PVDD
synthesizes realistic noise from the RAW domain with a physically meaningful
sensor noise model followed by ISP processing. Moreover, we also propose a new
video denoising framework, called Recurrent Video Denoising Transformer (RVDT),
which can achieve SOTA performance on PVDD and other current video denoising
benchmarks. RVDT consists of both spatial and temporal transformer blocks to
conduct denoising with long-range operations on the spatial dimension and
long-term propagation on the temporal dimension. Especially, RVDT exploits the
attention mechanism to implement the bi-directional feature propagation with
both implicit and explicit temporal modeling. Extensive experiments demonstrate
that 1) models trained on PVDD achieve superior denoising performance on many
challenging real-world videos than on models trained on other existing
datasets; 2) trained on the same dataset, our proposed RVDT can have better
denoising performance than other types of networks.
