Optica· 2018Q1
Learning to see through multimode fibers
- 388citations
- Q1SCImago
- 2018year
Short summary
Deep Neural Networks (DNNs) can reconstruct handwritten digits transmitted through up to 1km of multimode fiber (MMF) by analyzing speckle patterns.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep Neural Networks (DNNs) can reconstruct handwritten digits from MMF speckle patterns.
- Reconstruction was successful for fiber lengths up to 1km.
- Image reconstruction prior to classification enhances DNN performance.
- Classification accuracy decreases as MMF length increases.
AI-generated from the title and abstract; the full text is not read.
Abstract
We use Deep Neural Networks (DNNs) to classify and reconstruct a large database of handwritten digits from the intensity of the speckle patterns that result after the images propagated through multimode fibers (MMF). Images transmitted through fibers with up to 1km length were recovered. The ability of the network to recognize the input degraded with fiber length but the performance could be enhanced if the neural networks were trained to first reconstruct the image and then classify it rather than classify it directly from the speckle intensity.
The authors' abstract, as published at the source. Optica, 2018 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Acoustics and Ultrasonics
Acoustics and UltrasonicsPhysics and Astronomy