Image edge detection with a photonic spiking VCSEL-neuron
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 30, 2020
- Source ID
- 10.1364/oe.408747
Entities
People
- Antonio Hurtado
- Joshua Robertson
- Julián Bueno
- Matěj Hejda
- Shuiying Xiang
- Yahui Zhang
Organizations
- Engineering and Physical Sciences Research Council
- Horizon 2020
- National Natural Science Foundation of China
- Office of Naval Research Global