ArticleLight, science & applications2022
LOEN: Lensless opto-electronic neural network empowered machine vision.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Two-terminal β-GaLight, science & applications · 2026Article
- Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light.Nature communications · 2025Article
- Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light.Nature communications · 2024Article
- Spatially varying nanophotonic neural networks.Science advances · 2024Article
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Pre-sensor computing with compact multilayer optical neural network.Science advances · 2024Article
- Reconfigurable metamaterial processing units that solve arbitrary linear calculus equations.Nature communications · 2024Article
- An optical imager that can compute.Nature nanotechnology · 2024Article
- Multichannel meta-imagers for accelerating machine vision.Nature nanotechnology · 2024Article
- Redundancy-free integrated optical convolver for optical neural networks based on arrayed waveguide grating.Nanophotonics (Berlin, Germany) · 2024Article
- Article
- Opto-Electronic Hybrid Network Based on Scattering Layers.Sensors (Basel, Switzerland) · 2023Article
- Photonic multiplexing techniques for neuromorphic computing.Nanophotonics (Berlin, Germany) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.