Physics-guided neural network for single-shot real-time dynamic speckle imaging
TL;DR
The Bifurcated Speckle Network is introduced, a hybrid deep learning architecture that merges empirical data training with analytical physical models to mitigate noise artifacts of undersampled speckle measurements in RSSI and accelerates accurate flow parameter estimation for continuous biological monitoring.
Abstract
Laser speckle imaging is essential for continuous hemodynamic monitoring, but emerging high-speed modalities such as rolling shutter speckle imaging (RSSI) are severely bottlenecked by intensive computational demands and a high susceptibility to noise caused by statistical undersampling. To address these fundamental limitations, we introduce the Bifurcated Speckle Network (BiSNet), a hybrid deep learning architecture that merges empirical data training with analytical physical models to mitigate noise artifacts of undersampled speckle measurements in RSSI. To realize this hybrid training framework, BiSNet links localized architectural constraints with physics-guided optimization. By bounding the network’s receptive field, the model is forced to evaluate flow dynamics based on localized speckle fluctuations. This prevents the network from memorizing the macroscopic, uniform structures of physical phantoms. Also, a physics-guided loss embeds the RSSI forward model directly into the training process, enabling the network to learn the governing physics. This approach improves data efficiency, allowing the model to generalize across diverse flow conditions without requiring an exhaustive library of experimental phantom datasets. Validated using controlled microfluidic setups and in vivo cranial window models, BiSNet delivers high-fidelity, real-time hemodynamic mapping. By effectively suppressing undersampling noise and bypassing the latency of traditional iterative solvers, our proposed framework accelerates accurate flow parameter estimation for continuous biological monitoring.