ArticleScience advances2023
Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Music recognition based on diffractive neural networks.iScience · 2026Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks.Light, science & applications · 2025Article
- Improved all photonics diffraction neural network based on multi-channel integrated optical fibers.iScience · 2025Article
- The application of artificial intelligence-assisted technology in cultural and creative product design.Scientific reports · 2024Article
- Photonic diffractive generators through sampling noises from scattering media.Nature communications · 2024Article
- Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip.Nature communications · 2024Article
- A perspective on the artificial intelligence's transformative role in advancing diffractive optics.iScience · 2024Review
- Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits.Light, science & applications · 2024Article
- Integrated photonic encoder for low power and high-speed image processing.Nature communications · 2024Article
- All-optical image denoising using a diffractive visual processor.Light, science & applications · 2024Article
- Article
- Three-Dimensional Displacement Measurement of Micro-Milling Tool Based on Fiber Array Encoding.Micromachines · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Following the explosive growth of global data, there is an ever-increasing demand for high-throughput processing in image transmission systems. However, existing methods mainly rely on electronic circuits, which severely limits the transmission throughput. Here, we propose an end-to-end all-optical variational autoencoder, named photonic encoder-decoder (PED), which maps the physical system of image transmission into an optical generative neural network. By modeling the transmission noises as the variation in optical latent space, the PED establishes a large-scale high-throughput unsupervised optical computing framework that integrates main computations in image transmission, including compression, encryption, and error correction to the optical domain. It reduces the system latency of computation by more than four orders of magnitude compared with the state-of-the-art devices and transmission error ratio by 57% than on-off keying. Our work points to the direction for a wide range of artificial intelligence-based physical system designs and next-generation communications.
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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.