Translated object identification for efficient ghost imaging

Alice Ruget, Chané Moodley, Andrew Forbes, Jonathan Leach

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
11 Downloads (Pure)

Abstract

Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.
Original languageEnglish
Pages (from-to)41057-41068
Number of pages12
JournalOptics Express
Volume32
Issue number23
Early online date28 Oct 2024
DOIs
Publication statusPublished - 4 Nov 2024

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