Ultra-low-light computer vision using trained photon correlations
Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage of the knowledge that signal photons are spatially correlated whereas detector clicks due to noise are uncorrelated. However, in computer-vision tasks, the goal is often not ultimately to reconstruct an image, but to make inferences about a scene -- such as what object is present. Here we show how correlated-photon illumination can be used to gain an advantage in a hybrid optical-electronic computer-vision pipeline for object recognition. We demonstrate correlation-aware training (CAT): end-to-end optimization of...
| Authors | Mandar M. Sohoni, Jérémie Laydevant, Mathieu Ouellet et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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