Learning to see through multimode fibers
By N. Borhani, E. Kakkava, C. Moser, Demetri Psaltis. Optica, 2018
341 citations recorded by the source corpus.
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.
Subfields: Acoustics and Ultrasonics, Electrical and Electronic Engineering, Instrumentation
View Paper: https://www.semanticscholar.org/paper/bc05b16f1ac69de3f7eeae1cd58128373217ac19
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Acoustics and Ultrasonics
- S. Rotter
- Sylvain Gigan
- Ori Katz
- Y. Bromberg
- S. Popoff
- Ivo M. Vellekoop
- Allard P. Mosk
- Rebecca M. Willett
In Electrical and Electronic Engineering
- A. Dimakis
- Goëry Genty
- Ajil Jalal
- John M. Dudley
- Demetri Psaltis
- Sylvain Gigan
- Sergei K. Turitsyn
- Changhuei Yang
In Instrumentation
Citations in the corpus
- Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “ing [45], holography [46], [47], ghost imaging [48], imaging through scattering media [49]–[51], and non-line-of-sight imaging [52], which is illustrated in Figure 5.”
- Machine learning and applications in ultrafast photonics (2020, methodology): “Extension to spatial control for enhanced near-field interactions was also shown by combining a neural network with a genetic algorithm to optimize spectral-phase shaping of an incident field to achieve second harmonic generation hotspot switching in plasmonic nanoantennas 82 .”
- Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media (2018, background): “Another closely related line of work is using DL to imaging through multi-mode fibers (MMF) [8,12].”
- Roadmap on wavefront shaping and deep imaging in complex media (2021, methodology): “Using convolutional networks, Rahmani et al [202], Borhani et al [203] and Kakkava et al [204] were able to reconstruct sparse-like input images that were scrambled upon propagation through various MMF lengths up to 1 km. Figure 30(a) shows the optical setup that is used in the data collection step.”