Adaptive inverse mapping: a model-free semi-supervised learning approach towards robust imaging through dynamic scattering media

Abstract

Imaging through scattering media is a useful and yet demanding task since it involves solving for an inverse mapping from speckle images to object images. It becomes even more challenging when the scattering medium undergoes dynamic changes. Various approaches have been proposed in recent years. However, none of them are able to preserve high image quality without either assuming a finite number of sources for dynamic changes, assuming a thin scattering medium, or requiring access to both ends of the medium. In this paper, we propose an adaptive inverse mapping (AIP) method, which requires no prior knowledge of the dynamic change and only needs output speckle images after initialization. We show that the inverse mapping can be corrected through unsupervised learning if the output speckle images are followed closely. We test the AIP method on two numerical simulations: a dynamic scattering system formulated as an evolving transmission matrix and a telescope with a changing random phase mask at a defocused plane. Then we experimentally apply the AIP method to a multimode-fiber-based imaging system with a changing fiber configuration. Increased robustness in imaging is observed in all three cases. AIP method’s high imaging performance demonstrates great potential in imaging through dynamic scattering media.