UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate

• URL: http://arxiv.org/abs/2409.01985v4
• Date: Tue, 11 Feb 2025 18:09:35 GMT
• Title: UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
• Authors: Julián Tachella, Mike Davies, Laurent Jacques,
• Abstract summary: Many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone. We propose a new approach based on SURE, but unlike the standard SURE, does not require knowledge about the noise level. We show that the proposed estimator outperforms other existing self-supervised methods on various imaging inverse problems.
• Score: 12.289101615816595
• License:
• Abstract: Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Most existing methods cluster around two classes: i) Stein's Unbiased Risk Estimate (SURE) and similar approaches that assume full knowledge of the noise distribution, and ii) Noise2Self and similar cross-validation methods that require very mild knowledge about the noise distribution. The first class of methods tends to be impractical, as the noise level is often unknown in real-world applications, and the second class is often suboptimal compared to supervised learning. In this paper, we provide a theoretical framework that characterizes this expressivity-robustness trade-off and propose a new approach based on SURE, but unlike the standard SURE, does not require knowledge about the noise level. Throughout a series of experiments, we show that the proposed estimator outperforms other existing self-supervised methods on various imaging inverse problems.

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