无需噪声水平信息，用新方法提升图像重建自监督学习效果
UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
- 基于改进的无偏风险估计，不依赖噪声水平先验
- 在多种成像逆问题上优于现有自监督方法
- 适合真实场景中噪声未知的图像重建任务
近年来，许多用于图像重建的自监督学习方法被提出，可仅从含噪数据中学习，无需真实参考。现有方法主要分为两类：一类基于斯坦因无偏风险估计（SURE）等，需完全知晓噪声分布；另一类为Noise2Self等交叉验证方法，仅需对噪声分布有弱先验。前者在实际应用中常不切实际，因噪声水平往往未知；后者相比有监督学习性能通常较弱。本文提出一个理论框架，刻画表达能力与鲁棒性之间的权衡，并基于SURE提出新方法，无需噪声水平知识。通过一系列实验，证明该估计算法在多种成像逆问题上优于现有自监督方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。