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Time-Lapse Image Classification Using a Diffractive Neural Network
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DOI：10.1002/aisy.202200387.png)
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Abstract
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Diffractive deep neural networks (D(2)NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all-optical object classification and perform universal linear transformations. Herein, a time-lapse image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super-resolution applications; inspired by their success, a time-lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time-lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR-10 dataset. Time-lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all-optical processors.
Keywords:
diffractive deep neural networks ((DNN)-N-2)
diffractive computing
diffractive deep neural networks
diffractive networks
optical computing
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