The real-time positioning of an object on a microscopic scale is a significant challenge and remains difficult to apply. Many traditional imaging techniques exist but their axial resolution and/or their measurement range is often limited. We develop a novel high‐profile technology based on three pillars to meet these challenges. Using digital holography, we determine the correct focus distance on a large scale. Secondly, a new generation transformer neural networks processes the hologram giving in real-time (~30 frames per seconds) a submicrometric axial resolution, exceeding therefore the diffraction limit of the depth of field. Finally, the spatial structuring of the object allows us a nanometric lateral positioning by classical techniques, which will be sped up by a machine learning technique. Such high frame rates enable real-time processing in many different application scenarios.
We develop a novel high‐profile application of machine learning techniques by elevating digital holography and sensing in robotics to a new level. The extraction of unknown metrics such as focusing distance and in plane positioning without full image restoration from digital holograms is performed by pre‐processing approach in space‐domain and/or in Fourier‐domain, including real‐time constraints. Measuring a single hologram, we successfully determine the axial distance of a complex object to the 10x microscope objective over a range of 100 µm with an accuracy of 1.25 µm. We apply a machine learning technique to the hologram to speed up tracking in the plane of the pseudo-periodic target position up to several tens of frames per second (fps). Such high frame rates enable real-time processing in many different application scenarios.
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