Projects per year
Abstract
We report both experimentally and in theory on the detection of edge features in digital images with an artificial optical spiking neuron based on a vertical-cavity surface-emitting laser (VCSEL). The latter delivers fast (< 100 ps) neuron-like optical spikes in response to optical inputs pre-processed using convolution techniques; hence representing image feature information with a spiking data output directly in the optical domain. The proposed technique is able to detect target edges of different directionalities in digital images by applying individual kernel operators and can achieve complete image edge detection using gradient magnitude. Importantly, the neuromorphic (brain-like) spiking edge detection of this work uses commercially sourced VCSELs exhibiting responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the important telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-centre technologies.
Original language | English |
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Pages (from-to) | 37526-37537 |
Number of pages | 12 |
Journal | Optics Express |
Volume | 28 |
Issue number | 25 |
Early online date | 30 Nov 2020 |
DOIs | |
Publication status | Published - 7 Dec 2020 |
Keywords
- VCSEL
- image processing
- neuromorphic photonics
- edge detection
Projects
- 2 Active
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Doctoral Training Partnership (DTP 2016-2017 University of Strathclyde) | Robertson, Joshua
Hurtado, A., Strain, M. & Robertson, J.
EPSRC (Engineering and Physical Sciences Research Council)
1/10/17 → 1/04/21
Project: Research Studentship - Internally Allocated
Datasets
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Data for: "Image Edge Detection with a Photonic Spiking VCSEL-Neuron"
Robertson, J. (Creator), Zhang, Y. (Creator), Hejda, M. (Contributor) & Hurtado, A. (Supervisor), University of Strathclyde, 18 Nov 2020
DOI: 10.15129/551ef443-84fd-4c3a-8a1b-d381d0788cf2
Dataset