Time-domain photonic image processor based on speckle projection and reservoir computing
Tomoya Yamaguchi, Kohei Arai, Tomoaki Niiyama, Atsushi Uchida, Satoshi Sunada
Abstract
Abstract High-speed image processing is essential for many real-time applications. On-chip photonic neural network processors have the potential to speed up image processing, but their scalability is limited in terms of the number of input/output channels because high-density integration is challenging. Here, we propose a photonic time-domain image processing approach, where real-world visual information is compressively acquired through a single input channel. Thus, large-scale processing is enabled even when using a small photonic processor with limited input/output channels. The drawback of the time-domain serial operation can be mitigated using ultrahigh-speed data acquisition based on gigahertz-rate speckle projection. We combine it with a photonic reservoir computer and demonstrate that this approach is capable of dynamic image recognition at gigahertz rates. Furthermore, we demonstrate that this approach can also be used for high-speed learning-based imaging. The proposed approach can be extended to diverse applications, including target tracking, flow cytometry, and imaging of sub-nanosecond phenomena.