Fidelity

Inverse scattering problem

Speckle pattern

Pure mathematics

Diffuser (optics)

Field (mathematics)

Scattering

Telecommunications

Scalability

Computer science

Light scattering

Physics

Database

Mathematics

Optics

Mathematical analysis

Artificial intelligence

Inverse problem

Light source

Generalization

Computer vision

Photonics Research

Imaging through scattering media is one of the hotspots in the optical field, and impressive results have been demonstrated via deep learning (DL). However, most of the DL approaches are solely data-driven methods and lack the related physics prior, which results in a limited generalization capability. In this paper, through the effective combination of the speckle-correlation theory and the DL method, we demonstrate a physics-informed learning method in scalable imaging through an unknown thin scattering media, which can achieve high reconstruction fidelity for the sparse objects by training with only one diffuser. The method can solve the inverse problem with more general applicability, which promotes that the objects with different complexity and sparsity can be reconstructed accurately through unknown scattering media, even if the diffusers have different statistical properties. This approach can also extend the field of view (FOV) of traditional speckle-correlation methods. This method gives impetus to the development of scattering imaging in practical scenes and provides an enlightening reference for using DL methods to solve optical problems.

Imaging through unknown scattering media based on physics-informed learning