The physical origin and boundary of scalable imaging through scattering media: a deep learning-based exploration

Imaging through scattering media is valuable for many areas like biomedicine and communication. Recent progress enabled by deep learning (DL) has shown superiority especially in the model generalization. However, there is a lack of research to physically reveal the origin or define the boundary for such model scalability, which is important for utilizing DL approaches for scalable imaging despite scattering with high confidence. In this study, we find the amount of ballistic light component in the output field is the prerequisite for endowing a DL model with generalization capability by using a “one-to-all” training strategy, which offers a physically meaning invariance among the multi-source data. The findings are supported by both experimental and simulated tests, in which the roles of scattered and ballistic components are revealed in contributing to the origin and physical boundary of the model scalability. Experimentally, the generalization performance of the network is enhanced by increasing the portion of ballistic photons in detection. The mechanism understanding and practical guidance by our research are beneficial for developing DL methods for de-scattering with high adaptivity.