Deep speckle reassignment: towards bootstrapped imaging in complex scattering states with limited speckle grains

Abstract

Optical imaging through scattering media is a practical challenge with crucial applications in many fields. Many computational imaging methods have been designed for object reconstruction through opaque scattering layers, and remarkable recovery results have been demonstrated in the physical models or learning models. However, most of the imaging approaches are dependent on relatively ideal states with a sufficient number of speckle grains and adequate data volume. Here, the in-depth information with limited speckle grains has been unearthed with speckle reassignment and a bootstrapped imaging method is proposed for reconstruction in complex scattering states. Benefiting from the bootstrap priors-informed data augmentation strategy with a limited training dataset, the validity of the physics-aware learning method has been demonstrated and the high-fidelity reconstruction results through unknown diffusers are obtained. This bootstrapped imaging method with limited speckle grains broadens the way to highly scalable imaging in complex scattering scenes and gives a heuristic reference to practical imaging problems.