DeepSCI: scalable speckle correlation imaging using physics-enhanced deep learning

Abstract

In this Letter we present a physics-enhanced deep learning approach for speckle correlation imaging (SCI), i.e., DeepSCI. DeepSCI incorporates the theoretical model of SCI into both the training and test stages of a neural network to achieve interpretable data preprocessing and model-driven fine-tuning, allowing the full use of data and physics priors. It can accurately reconstruct the image from the speckle pattern and is highly scalable to both medium perturbations and domain shifts. Our experimental results demonstrate the suitability and effectiveness of DeepSCI for solving the problem of limited generalization generally encountered in data-driven approaches.