无需真实数据，用自监督方法精准评估图像恢复的不确定性。
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
- 利用泊松无偏风险估计实现无真值自校准
- 在去噪和去模糊任务中性能接近有真值监督的方法
- 适合缺乏标注数据的图像恢复场景
图像恢复问题通常为不适定问题，导致重建图像存在显著不确定性。准确量化这种不确定性对可靠解读重建结果至关重要。然而，现有图像恢复方法普遍缺乏不确定性量化能力。分位数预测提供了一个严格的框架，可为图像恢复方法添加精确的不确定性估计，但通常需要大量真实标签数据进行校准。本文提出一种针对泊松成像问题的自监督分位数预测方法，利用泊松无偏风险估计（Poisson Unbiased Risk Estimator）消除对真实数据的需求。所提方法为任意病态的泊松线性成像问题提供自校准分位数预测，尤其在与直接基于测量数据训练的现代自监督图像恢复技术结合时表现优异。通过数值实验验证了该方法在图像去噪和去模糊任务中的有效性，其性能与依赖真实标签的监督式分位数预测方法相当。
原文摘要 · Abstract (English)
Image restoration problems are often ill-posed, leading to significant uncertainty in reconstructed images. Accurately quantifying this uncertainty is essential for the reliable interpretation of reconstructed images. However, image restoration methods often lack uncertainty quantification capabilities. Conformal prediction offers a rigorous framework to augment image restoration methods with accurate uncertainty quantification estimates, but it typically requires abundant ground truth data for calibration. This paper presents a self-supervised conformal prediction method for Poisson imaging problems which leverages Poisson Unbiased Risk Estimator to eliminate the need for ground truth data. The resulting self-calibrating conformal prediction approach is applicable to any Poisson linear imaging problem that is ill-conditioned, and is particularly effective when combined with modern self-supervised image restoration techniques trained directly on measurement data. The proposed method is demonstrated through numerical experiments on image denoising and deblurring; its performance are comparable to supervised conformal prediction methods relying on ground truth data.
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