用噪声数据反推隐藏结构，让低质量成像也能看清本质。
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations
- 用随机轻量神经网络+分位数校准，边去噪边生成可信不确定性
- 在无标签情况下发现空间与化学特征，还原真实物性分布
- 适合资源受限的科学成像，助研究人员优化实验设计
科学成像常需长时间采集以获得高质量数据，尤其在探测复杂异质系统时。但为提升通量而缩短采集时间会引入显著噪声。本文提出一种机器学习方法，不仅能对低质量测量进行去噪并提供校准的不确定性边界，还能揭示潜在空间中的涌现结构。通过使用基于分位数校准的轻量级随机结构神经网络集成，该方法在无需标签或分割的前提下，实现可靠去噪，并挖掘出可解释的空间与化学特征。不同于仅关注图像修复的传统方法，本框架利用去噪过程本身驱动有意义表征的生成。我们在真实世界的地质生物化学成像数据上验证了该方法，展示了其在资源受限条件下支持自信解读并指导实验设计的能力。
原文摘要 · Abstract (English)
Scientific imaging often involves long acquisition times to obtain high-quality data, especially when probing complex, heterogeneous systems. However, reducing acquisition time to increase throughput inevitably introduces significant noise into the measurements. We present a machine learning approach that not only denoises low-quality measurements with calibrated uncertainty bounds, but also reveals emergent structure in the latent space. By using ensembles of lightweight, randomly structured neural networks trained via conformal quantile regression, our method performs reliable denoising while uncovering interpretable spatial and chemical features -- without requiring labels or segmentation. Unlike conventional approaches focused solely on image restoration, our framework leverages the denoising process itself to drive the emergence of meaningful representations. We validate the approach on real-world geobiochemical imaging data, showing how it supports confident interpretation and guides experimental design under resource constraints.
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