Research On Topological Charge Detection Of Optical Vortex Beams Using Shack-Hartmann Wavefront Sensor

Recently,optical vortex(OV)beams have received extensive attention and research,because of their unique properties and broad application prospects.In their related research,how to accurately and efficiently detect the key parameter,i.e.,topological charge(TC),is always a hot spot.As OV beams are being applied in more and more fields,their complex application scenarios bring new requirements and challenges to the TC detection.In this paper,we focus on the applications such as optical communication,optical measurement,etc.And based on Shack-Hartmann Wavefront Sensor(SH-WFS),we explore several technical problems and difficulties of the TC detection in real application scenarios,starting from the core requirement of the far-field condition.The main content and contributions of the paper are as follows:Firstly,we design and build an SH-WFS based detection system for the OV beams under the far-field condition.Combined with the numerical calculation and simulation of the aberration environments such as the atmospheric turbulence,we enable the detection system to not only flexibly change the OV beam’s OV number and TC values,but also freely switch between the presence or absence of aberration and a variety of aberration environments.Moreover,we discover and demonstrate the misaligned error in the SH-WFS.And based on the experimental observations,we adjust it so that the detection accuracy is sufficiently improved.Secondly,for the OV beam’s special annular distribution in the far-field,we propose a TC detection method based on the SH-WFS,namely the Maximum Average Intensity Circle Method(MAICM),to accurately detect the TC value of a single-OV beam in the far-field.Extensive experiments illustrate that this method can better deal with the problem of SH-WFS’s few effective data caused by the annular distribution,and achieves 100% detection accuracy for OV beams with TC values in the range of ±20.In addition,the MAICM is robust to a variety of aberration environments,and its detection speed of over 60 FPS can meet the real-time requirements.Thirdly,for the strong aberration environment,we propose the Watershed Transformed MAICM(WT-MAICM),based on the error analysis on MAICM and its comparisons to the other methods.Compared to the MAICM,the WT-MAICM introduces the watershed transformation to guide the closed-path extraction in MAICM,which significantly improves the robustness against the aberrations.Besides,its detection accuracy under the aberration-free condition is not lost,and its detection speed is also not sacrificed much.Finally,for the multi-OVs situation,we propose a deep-learning based TC detection method,i.e.,the U-Net-based Intensity Ridge Extraction Method(U-IREM).The experimental results show that in the case of multi-OVs,U-IREM can simultaneously detect the TC values of multiple OVs in the OV beam,with the detection accuracy of 100%.