Enhancing Speckle Metrology with Diffusion Denoising in Photon-Starved Regimes

Abstract

Laser speckle is a powerful tool for precision metrology that enables highly sensitive measurements of light sources and subtle environmental perturbations. Many applications require operation in photon-limited regimes, for example when using low-power illumination or in spectral regions where sensitive detectors are unavailable. In these conditions, the structured speckle pattern that encodes the signal becomes challenging to disentangle from measurement noise, severely degrading performance. Here, we introduce a denoising framework to separate measurement noise from the underlying speckle structure in low-signal data. Using a hybrid pre-training and experimental fine-tuning strategy, the model is adapted using a small experimental dataset and integrates directly with existing speckle metrology pipelines. Applied to femtometre-scale wavelength sensing using an integrating sphere, the approach reduces root-mean-square error in low-signal conditions by up to 72% and enables accurate reconstruction where conventional speckle metrology fails.