  In recent years, the supervised learning strategy for real noisy image
denoising has been emerging and has achieved promising results. In contrast,
realistic noise removal for raw noisy videos is rarely studied due to the lack
of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes
cannot be captured with a long-exposure shutter or averaging multi-shots as was
done for static images. In this paper, we solve this problem by creating
motions for controllable objects, such as toys, and capturing each static
moment for multiple times to generate clean video frames. In this way, we
construct a dataset with 55 groups of noisy-clean videos with ISO values
ranging from 1600 to 25600. To our knowledge, this is the first dynamic video
dataset with noisy-clean pairs. Correspondingly, we propose a raw video
denoising network (RViDeNet) by exploring the temporal, spatial, and channel
correlations of video frames. Since the raw video has Bayer patterns, we pack
it into four sub-sequences, i.e RGBG sequences, which are denoised by the
proposed RViDeNet separately and finally fused into a clean video. In addition,
our network not only outputs a raw denoising result, but also the sRGB result
by going through an image signal processing (ISP) module, which enables users
to generate the sRGB result with their favourite ISPs. Experimental results
demonstrate that our method outperforms state-of-the-art video and raw image
denoising algorithms on both indoor and outdoor videos.
