无需真实数据，用自监督方法恢复被截断的音视频信号。
Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
- 假设信号分布对幅度变化不变，设计自监督损失函数。
- 在音视频数据上表现接近有监督方法。
- 适合无真实参考数据的逆问题重建场景。
基于学习的方法在求解逆问题中已广泛应用，但其在实际应用中的部署常受限于缺乏真实参考数据用于训练。近期的自监督学习策略提供了一种有前景的替代方案，可避免对真实数据的需求。然而，现有方法大多局限于线性逆问题。本文将自监督学习扩展至从截断测量中恢复音频和图像这一非线性逆问题，假设信号分布对幅度变化近似不变。我们给出了仅依赖饱和信号即可学习重建的充分条件，并提出一种可用于训练重建网络的自监督损失。在音频与图像数据上的实验表明，该方法虽仅使用截断测量进行训练，效果几乎等同于全监督方法。
原文摘要 · Abstract (English)
Learning based methods are now ubiquitous for solving inverse problems, but their deployment in real-world applications is often hindered by the lack of ground truth references for training. Recent self-supervised learning strategies offer a promising alternative, avoiding the need for ground truth. However, most existing methods are limited to linear inverse problems. This work extends self-supervised learning to the non-linear problem of recovering audio and images from clipped measurements, by assuming that the signal distribution is approximately invariant to changes in amplitude. We provide sufficient conditions for learning to reconstruct from saturated signals alone and a self-supervised loss that can be used to train reconstruction networks. Experiments on both audio and image data show that the proposed approach is almost as effective as fully supervised approaches, despite relying solely on clipped measurements for training.
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