Time-lapse image classification boosts diffractive optical network inference accuracy

Md Sadman Sakib Rahman; Aydogan Ozcan

doi:10.1117/12.3001804

13 March 2024 Time-lapse image classification boosts diffractive optical network inference accuracy

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030A (2024) https://doi.org/10.1117/12.3001804
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

We enhance the accuracy of a diffractive optical network through time-lapse-based inference, which exploits the information diversity obtained by introducing controlled or random displacements between the object and the diffractive network, relative to each other. The numerical blind testing accuracy achieved using this time-lapse-based inference scheme on CIFAR-10 images reached >62%, representing the highest accuracy achieved so far on this dataset using a single diffractive network. Beyond image classification, this framework could also open doors to broader utilization of diffractive networks in tasks involving all-optical spatiotemporal information processing, paving the way for advanced visual computing paradigms.

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Md Sadman Sakib Rahman and Aydogan Ozcan "Time-lapse image classification boosts diffractive optical network inference accuracy", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030A (13 March 2024); https://doi.org/10.1117/12.3001804

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KEYWORDS

Optical networks

Image classification

Data processing

Neural networks

Geometrical optics

Image sensors

Information fusion

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