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Self-evolving ghost imaging

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Document pages: 5 pages

Abstract: Ghost imaging can capture 2D images with a point detector instead of an arraysensor. It therefore offers a solution to the challenge of building area formatsensors in wavebands where such sensors are difficult and expensive to produceand opens up new imaging modalities due to high-performance single-pixeldetectors. Traditionally, ghost imaging retrieves the image of an objectoffline, by correlating measured light intensities and applied illuminatingpatterns. Here we present a feedback-based approach for online updating of theimaging result that can bypass post-processing, termed self-evolving ghostimaging (SEGI). We introduce a genetic algorithm to optimize the illuminationpatterns in real-time to match the objects shape according to the measuredtotal light intensity. We theoretically and experimentally demonstrate thisconcept for static and dynamic imaging. This method opens new perspectives forreal-time ghost imaging in applications such as remote sensing (e.g. machinevision, LiDAR systems in autonomous vehicles) and biological imaging.

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