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[2602.22279] Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
Summary
This paper presents a novel approach to reconstruct audio and images from clipped measurements using self-supervised learning, addressing the challenges of training without ground truth data.
Why It Matters
The research is significant as it expands the application of self-supervised learning to non-linear inverse problems, which are common in real-world scenarios. This method could improve the quality of audio and image processing in various fields, including telecommunications and media.
Key Takeaways
- Introduces a self-supervised learning method for reconstructing saturated audio and images.
- Demonstrates that the approach is nearly as effective as fully supervised methods.
- Provides sufficient conditions for learning from clipped measurements.
- Offers a new self-supervised loss function for training reconstruction networks.
- Highlights the potential for real-world applications in audio and image processing.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.22279 (eess) [Submitted on 25 Feb 2026] Title:Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging Authors:Victor Sechaud, Laurent Jacques, Patrice Abry, Julián Tachella View a PDF of the paper titled Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging, by Victor Sechaud and 3 other authors View PDF HTML (experimental) Abstract: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. Subjects: Image and Video Processing (eess.IV); Artifi...