Deep learning denoising unlocks quantitative insights in operando materials microscopy

2025-10-31

AI Summary

In situ materials microimaging suffers from measurement noise, degrading spatial resolution and compromising quantitative accuracy. To address this, we propose an unsupervised deep learning denoising framework that innovatively integrates physics-driven partial differential equation (PDE) constraints into the optimization objective, ensuring physical fidelity and mitigating model uncertainty—without requiring ground-truth clean labels. The method is broadly applicable across multimodal, multiscale imaging modalities—including scanning transmission X-ray microscopy (STXM), optical microscopy, and neutron radiography—achieving cross-modal generalization via joint optimization of simulation-based pretraining and PDE regularization. Experiments demonstrate up to 80% reduction in noise-induced variability. The approach successfully resolves nanoscale chemical heterogeneity in LiFePO₄ cathodes and enables high-precision, fully automated segmentation and phase classification of graphite anodes. These advances significantly enhance the reliability and interpretability of in situ quantitative analysis.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Reducing noise limits in operando microscopy imaging
• Preserving physical fidelity during quantitative materials analysis
• Enabling precise measurement of nanoscale material heterogeneity

Innovation

• Unsupervised deep learning denoising framework
• Preserves physical fidelity with minimal bias
• Reduces noise-induced variability by 80%