Classification and reconstruction of spatially overlapping phase images using diffractive optical networks.

ABSTRACT: Diffractive optical networks unify wave optics and deep learning to all-optically compute a given machine learning or computational imaging task as the light propagates from the input to the output plane. Here, we report the design of diffractive optical networks for the classification and reconstruction of spatially overlapping, phase-encoded objects. When two different phase-only objects spatially overlap, the individual object functions are perturbed since their phase patterns are summed up. The retrieval of the underlying phase images from solely the overlapping phase distribution presents a challenging problem, the solution of which is generally not unique. We show that through a task-specific training process, passive diffractive optical networks composed of successive transmissive l

SUBMITTER: Mengu D 

PROVIDER: S-EPMC9120207 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature