Validation of pre-compensation under point-ahead-angle in a 1 km free-space propagation experiment

Brady, Aoife; Rößler, Conrad; Leonhard, Nina; Gier, Marlies; Böttner, Paul; Eberhardt, Ramona; Tünnermann, Andreas; Reinlein, Claudia

doi:10.1364/oe.27.017840

Cited by 8 publications

(1 citation statement)

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“…The Shack-Hartmann sensor can capture data in real time, which makes it suitable for real-time systems [5]. This method has been used for the successful demonstration of adaptive optics correction of atmospheric turbulence in breadboard and free-space experiments, e.g., classical laser [6][7][8] and quantum communications [9,10]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Implementing a Hybrid Method for Shack–Hartmann Wavefront Spots Labeling on FPGA

Abdullah,

Brady,

Heinig

et al. 2024

Electronics

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This paper presents a real-time implementation of a hybrid connected component labeling method for processing the Shack–Hartmann wavefront sensor’s images for an adaptive optics (AO) system. The output image of a wavefront sensor is an image of spots. During the sensor’s operation, it can happen that highly distorted wavefronts (WF) may cause the spots to shift outside of their sub-aperture, which may lead to the reduction of the AO system performance. This article explains the benefits of high-performance computing and parallel processing of a field programmable gate array (FPGA). The objective is to calculate the centroids of these spots. A hybrid labeling method was investigated to fulfill this purpose. First, this method was implemented using a forward and backward scan with a respective mask for each scan. Additionally, a relabeling process is applied after labeling each line, and it is carried out in both directions. After labeling, several processing units were implemented in parallel to calculate centroids. Each unit is responsible for calculating the centroid of one label. The system runs in real time with a latency of one frame, which means the output image is a fusion of a current frame and the centroids of the previous frame. Forward and backward labeling requires a large amount of memory, which is the reason for limiting the investigation to forward labeling only. The forward labeling was successfully implemented, and the centroids were detected under minimum spot distortion conditions. This forward labeling implementation also runs in real time with significant latency reduction to calculate the centroids, which leads to minimizing the overall AO system latency, enabling faster computation and correction in addition to reducing the memory usage to 1% when compared to the forward and backward labeling usage of 81% as an advantage for the hardware implementation.

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