  Video denoising aims to recover high-quality frames from the noisy video.
While most existing approaches adopt convolutional neural networks~(CNNs) to
separate the noise from the original visual content, however, CNNs focus on
local information and ignore the interactions between long-range regions in the
frame. Furthermore, most related works directly take the output after basic
spatio-temporal denoising as the final result, leading to neglect the
fine-grained denoising process. In this paper, we propose a Dual-stage
Spatial-Channel Transformer for coarse-to-fine video denoising, which inherits
the advantages of both Transformer and CNNs. Specifically, DSCT is proposed
based on a progressive dual-stage architecture, namely a coarse-level and a
fine-level stage to extract dynamic features and static features, respectively.
At both stages, a Spatial-Channel Encoding Module is designed to model the
long-range contextual dependencies at both spatial and channel levels.
Meanwhile, we design a Multi-Scale Residual Structure to preserve multiple
aspects of information at different stages, which contains a Temporal Features
Aggregation Module to summarize the dynamic representation. Extensive
experiments on four publicly available datasets demonstrate our proposed method
achieves significant improvements compared to the state-of-the-art methods.
