Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?

TL;DR

This paper compares self-supervised and synthetic dataset-based training for video denoising, finding self-supervision on real data generally outperforms synthetic supervision, especially under normal lighting conditions.

Contribution

The study provides a comprehensive comparison between self-supervised and synthetic supervised training methods for real raw video denoising, highlighting the advantages of self-supervision.

Findings

Self-supervision on real data outperforms synthetic supervision.

Synthetic ground truth generation impacts performance more than noise modeling.

Performance gap narrows in normal illumination conditions.

Abstract

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.