  In recent years, raw video denoising has garnered increased attention due to
the consistency with the imaging process and well-studied noise modeling in the
raw domain. However, two problems still hinder the denoising performance.
Firstly, there is no large dataset with realistic motions for supervised raw
video denoising, as capturing noisy and clean frames for real dynamic scenes is
difficult. To address this, we propose recapturing existing high-resolution
videos displayed on a 4K screen with high-low ISO settings to construct
noisy-clean paired frames. In this way, we construct a video denoising dataset
(named as ReCRVD) with 120 groups of noisy-clean videos, whose ISO values
ranging from 1600 to 25600. Secondly, while non-local temporal-spatial
attention is beneficial for denoising, it often leads to heavy computation
costs. We propose an efficient raw video denoising transformer network
(RViDeformer) that explores both short and long-distance correlations.
Specifically, we propose multi-branch spatial and temporal attention modules,
which explore the patch correlations from local window, local low-resolution
window, global downsampled window, and neighbor-involved window, and then they
are fused together. We employ reparameterization to reduce computation costs.
Our network is trained in both supervised and unsupervised manners, achieving
the best performance compared with state-of-the-art methods. Additionally, the
model trained with our proposed dataset (ReCRVD) outperforms the model trained
with previous benchmark dataset (CRVD) when evaluated on the real-world outdoor
noisy videos. Our code and dataset will be released after the acceptance of
this work.
