Deep learning denoising unlocks quantitative insights in operando materials microscopy
This addresses the problem of noise-limited quantitative analysis in materials microscopy for researchers, offering a modality-agnostic enhancement that is incremental but broadly applicable.
The paper tackles noise in operando microscopy that limits resolution and quantitative analysis by introducing an unsupervised deep learning denoising framework, demonstrating it reduces noise-induced variability by nearly 80% in neutron radiography and enables nanoscale insights in various microscopy techniques.
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.