  Supervised training has led to state-of-the-art results in image and video
denoising. However, its application to real data is limited since it requires
large datasets of noisy-clean pairs that are difficult to obtain. For this
reason, networks are often trained on realistic synthetic data. More recently,
some self-supervised frameworks have been proposed for training such denoising
networks directly on the noisy data without requiring ground truth. On
synthetic denoising problems supervised training outperforms self-supervised
approaches, however in recent years the gap has become narrower, especially for
video. In this paper, we propose a study aiming to determine which is the best
approach to train denoising networks for real raw videos: supervision on
synthetic realistic data or self-supervision on real data. A complete study
with quantitative results in case of natural videos with real motion is
impossible since no dataset with clean-noisy pairs exists. We address this
issue by considering three independent experiments in which we compare the two
frameworks. We found that self-supervision on the real data outperforms
supervision on synthetic data, and that in normal illumination conditions the
drop in performance is due to the synthetic ground truth generation, not the
noise model.
