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Image sensing with multilayer nonlinear optical neural networks

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Abstract

Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object’s position or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that—instead of performing imaging—act as encoders that optically compress images into low-dimensional spaces by extracting salient features; however, the performance of these encoders is typically limited by their linearity. Here we report a nonlinear, multilayer optical neural network (ONN) encoder for image sensing based on a commercial image intensifier as an optical-to-optical nonlinear activation function. This nonlinear ONN outperforms similarly sized linear optical encoders across several representative tasks, including machine-vision benchmarks, flow-cytometry image classification and identification of objects in a three-dimensionally printed real scene. For machine-vision tasks, especially those featuring incoherent broadband illumination, our concept allows for a considerable reduction in the requirement of camera resolution and electronic post-processing complexity. In general, image pre-processing with ONNs should enable image-sensing applications that operate accurately with fewer pixels, fewer photons, higher throughput and lower latency.

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Fig. 1: A multilayer optical-neural-network encoder as a frontend for image sensing.
Fig. 2: Comparison between linear and nonlinear ONN encoders on diverse image classification tasks.
Fig. 3: Nonlinear ONN encoders trained for classification can be reused for diverse image-sensing tasks by training only new digital backends.
Fig. 4: Simulations of performance scaling with deeper nonlinear optical neural network encoders for ten-class cell-organelle classification.

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Data availability

The demonstration data for data gathering, as well as training data for the all-optical/digital neural networks, are available at https://github.com/mcmahon-lab/Image-sensing-with-multilayer-nonlinear-optical-neural-networks.

Code availability

All of the data generated, and code used, in this work are available at https://doi.org/10.5281/zenodo.6888985.

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Acknowledgements

We wish to thank NTT Research for their financial and technical support (to T.W., L.G.W., S.-Y.M., T.O. and P.L.M.). Portions of this work were supported by the National Science Foundation (award no. CCF-1918549 to T.W., M.M. Stein and P.L.M.), a Kavli Institute at Cornell instrumentation grant (to T.W. and P.L.M.), and a David and Lucile Packard Foundation Fellowship (to P.L.M.). P.L.M. acknowledges membership of the CIFAR Quantum Information Science Program as an Azrieli Global Scholar. T.W. acknowledges the partial support from Schmidt Futures via an Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship to Cornell University. We acknowledge helpful discussions with A. Senanian, B. Malia, F. Presutti, V. Kremenetski, S. Prabhu, A. Barth, R. Oliver, and D. Schraivogel. We also acknowledge S. Sohoni for help with figure design.

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Author notes

  1. These authors contributed equally: Tianyu Wang, Mandar M. Sohoni.

Authors and Affiliations

  1. School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA

    Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Martin M. Stein, Shi-Yuan Ma, Tatsuhiro Onodera, Maxwell G. Anderson & Peter L. McMahon

  2. Physics & Informatics Laboratories, NTT Research Inc., Sunnyvale, CA, USA

    Logan G. Wright & Tatsuhiro Onodera

  3. Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY, USA

    Peter L. McMahon

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Contributions

T.W., L.G.W., M.M. Sohoni and P.L.M. conceived the project and designed the experiments. M.M. Sohoni and T.W. built and performed the experiments on the nonlinear and linear ONN encoders, and analysed the data. T.W. performed the extended cell-organelle simulations. M.M. Stein performed the neural architecture search for QuickDraw reconstruction. S-Y.M. and T.O. aided in simulations of deep optical encoders. M.G.A. assisted with 3D-scene modelling. L.G.W., T.W., M.M. Sohoni and P.L.M. wrote the manuscript. P.L.M. and L.G.W. supervised the project.

Corresponding authors

Correspondence to Tianyu Wang, Mandar M. Sohoni, Logan G. Wright or Peter L. McMahon.

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Competing interests

T.W., M.M. Sohoni, L.G.W. and P.L.M. are listed as inventors on a US provisional patent application (serial no. 63/392,042) on nonlinear optical neural network pre-processors for imaging and image sensing. The other authors declare no competing interests.

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Nature Photonics thanks Jacques Carolan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wang, T., Sohoni, M.M., Wright, L.G. et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photon. 17, 408–415 (2023). https://doi.org/10.1038/s41566-023-01170-8

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