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.
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