Extended Learning Robustness for High-Fidelity Human Face Imaging from Spatiotemporally Decorrelated Speckles

Imaging within or through scattering media has long been a coveted yet challenging pursuit. Researchers have made significant progress in extracting target information from speckles, primarily by characterizing the transmission matrix of the scattering medium or employing neural networks. However, the fidelity of the retrieved images is compromised when the medium’s status changes due to intrinsic motion or external perturbations. This variability leads to decorrelation between training and testing data, hindering the practical applications of these frameworks. In this study, we propose a generative adversarial network (GAN)-based framework with extended robustness, which is designed to address the spatiotemporal instabilities of scattering media and the resultant decorrelation between training and testing data. Experiments demonstrate that our GAN can retrieve high-fidelity face images from speckles, even when the scattering medium undergoes unknown changes after training. Notably, our GAN outperforms existing methods by non-holographically retrieving images from unstable scattering media and effectively addressing speckle decorrelation, even after prolonged system inactivity (up to 37 h in experiments, but can be longer if needed). This resilience opens venues for pre-trained networks to maintain effectiveness over time, and can broaden the scope of learning-based methodologies in deep tissue imaging and sensing under extreme environmental conditions.