Diffractive networks classify and reconstruct overlapping phase objects

Deniz Mengu; Muhammed Veli; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2609491

2 March 2022 Diffractive networks classify and reconstruct overlapping phase objects

Deniz Mengu, Muhammed Veli, Yair Rivenson, Aydogan Ozcan

Proceedings Volume PC11970, Quantitative Phase Imaging VIII; PC119700A (2022) https://doi.org/10.1117/12.2609491
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

We report diffractive optical networks designed through a task-specific training process to classify and reconstruct spatially-overlapping phase images. Trained with ~550-million unique combinations of spatially-overlapping phase-encoded handwritten digits (MNIST), our blind testing achieves >85.8% accuracy for all-optical, simultaneous classification of two overlapping phase images of new/unseen handwritten digits. We also demonstrate the reconstruction of these phase images based on a shallow electronic neural network that uses as its input the highly-compressed optical signals synthesized by the diffractive network with ~20-65 times less number of pixels. This framework might find applications in computational imaging, on-chip microscopy and quantitative phase imaging fields.

Conference Presentation

Citation Download Citation

Deniz Mengu, Muhammed Veli, Yair Rivenson, and Aydogan Ozcan "Diffractive networks classify and reconstruct overlapping phase objects", Proc. SPIE PC11970, Quantitative Phase Imaging VIII, PC119700A (2 March 2022); https://doi.org/10.1117/12.2609491

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