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

TL;DR

This paper demonstrates a diffractive optical network capable of classifying and reconstructing overlapping phase images all-optically, achieving high accuracy and efficient reconstruction, with potential applications in imaging and microscopy.

Contribution

The authors design a passive diffractive optical network that classifies and reconstructs overlapping phase images, a novel approach in optical computing and imaging.

Findings

Achieves >85.8% accuracy in classifying overlapping phase images.

Uses a shallow neural network for rapid phase image reconstruction.

Trained on 550 million phase-encoded handwritten digit combinations.

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 networks composed of successive transmissive layers can all-optically and simultaneously classify two different randomly-selected, spatially overlapping phase images at the input. After trained with ~550 million unique combinations of phase-encoded handwritten digits from the MNIST dataset, our blind testing results reveal that the diffractive network achieves an accuracy of >85.8% for all-optical classification of two overlapping phase images of new handwritten digits. In addition to all-optical classification of overlapping phase objects, we also demonstrate the reconstruction of these phase images based on a shallow electronic neural network that uses the highly compressed output of the diffractive network as its input (with e.g., ~20-65 times less number of pixels) to rapidly reconstruct both of the phase images, despite their spatial overlap and related phase ambiguity. The presented phase image classification and reconstruction framework might find applications in e.g., computational imaging, microscopy and quantitative phase imaging fields.