Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel self-supervised learning framework for reconstructing signals from saturated data, demonstrating strong theoretical foundations and practical performance. The approach addresses critical challenges in inverse problems, making a meaningful contribution to the field of machine learning.
Methodology
The paper proposes a self-supervised learning approach to recover audio and images from clipped measurements, extending existing self-supervised techniques that are typically limited to linear inverse problems. The authors provide theoretical guarantees for model identification and signal recovery, leveraging the assumption of amplitude invariance. The methodology is well-structured, introducing a self-supervised loss function that incorporates measurement consistency and equivariance to amplitude changes, which is a significant advancement in the field of inverse problems.
Results & evaluation
The experiments are comprehensive, covering both audio and image data, and demonstrate that the proposed method achieves performance comparable to fully supervised methods despite being trained solely on clipped measurements. The use of multiple datasets and metrics (SDR, PSNR, PESQ, NIQE) strengthens the evaluation. However, the paper could benefit from more extensive comparisons with a broader range of existing methods and datasets.
Reproducibility
The paper provides a detailed description of the network architecture (U-Net) and the loss functions used, which aids reproducibility. However, the absence of a publicly available code repository limits the ease of replication of the experiments.
Limitations
A notable limitation is the reliance on the assumption of amplitude invariance, which may not hold in all practical scenarios. Additionally, the experiments could be expanded to include more diverse datasets and conditions to further validate the robustness of the proposed method.
Broader impact
The proposed method has significant implications for real-world applications in audio processing and image reconstruction, particularly in scenarios where obtaining ground truth data is challenging. This work could pave the way for more robust self-supervised learning techniques in various fields, including medical imaging and remote sensing. The paper presents a novel self-supervised learning framework for reconstructing signals from saturated data, demonstrating strong theoretical foundations and practical performance. The approach addresses critical challenges in inverse problems, making a meaningful contribution to the field of machine learning.
Author of this paper? Send a correction.