用深度学习降噪，让实时材料显微镜看清纳米级化学变化。
Deep learning denoising unlocks quantitative insights in operando materials microscopy
- 用无监督深度学习去除显微图像噪声，保持物理真实性。
- 降噪后锂离子传输不均性减少近80%，纳米结构差异清晰可见。
- 适用于多种显微技术，适合材料动态过程研究者使用。
原位显微技术可直接揭示功能材料中动态的化学与物理过程，但测量噪声限制了有效分辨率并影响定量分析。本文提出一种通用框架，将基于无监督深度学习的降噪方法集成到跨模态、多尺度的定量显微流程中。通过模拟数据验证，该方法在偏微分方程约束优化下保持物理保真度，引入极小偏差，并降低模型学习不确定性。实际应用中，降噪使锂铁磷酸盐（LFP）的扫描透射X射线显微镜（STXM）揭示出纳米级化学与结构异质性；在石墨电极光学显微中实现颗粒自动分割与相分类；在中子断层扫描中将噪声引起的变异减少近80%，从而解析出非均匀的锂运输行为。结果表明，深度降噪是一种强健、模态无关的增强手段，显著提升原位定量成像能力，拓展了以往受噪声限制的技术边界。
原文摘要 · Abstract (English)
Operando microscopy provides direct insight into the dynamic chemical and physical processes that govern functional materials, yet measurement noise limits the effective resolution and undermines quantitative analysis. Here, we present a general framework for integrating unsupervised deep learning-based denoising into quantitative microscopy workflows across modalities and length scales. Using simulated data, we demonstrate that deep denoising preserves physical fidelity, introduces minimal bias, and reduces uncertainty in model learning with partial differential equation (PDE)-constrained optimization. Applied to experiments, denoising reveals nanoscale chemical and structural heterogeneity in scanning transmission X-ray microscopy (STXM) of lithium iron phosphate (LFP), enables automated particle segmentation and phase classification in optical microscopy of graphite electrodes, and reduces noise-induced variability by nearly 80% in neutron radiography to resolve heterogeneous lithium transport. Collectively, these results establish deep denoising as a powerful, modality-agnostic enhancement that advances quantitative operando imaging and extends the reach of previously noise-limited techniques.
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