12 January 2018 Linear dependence between the wavefront gradient and the masked intensity for the point source with a CCD sensor
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Abstract
In contrast to the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system doesn’t need a WFS to measure the wavefront aberrations. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. The model-based WFSless system has a great potential in real-time correction applications because of its fast convergence. The control algorithm of the model-based WFSless system is based on an important theory result that is the linear relation between the Mean-Square Gradient (MSG) magnitude of the wavefront aberration and the second moment of the masked intensity distribution in the focal plane (also called as Masked Detector Signal-MDS). The linear dependence between MSG and MDS for the point source imaging with a CCD sensor will be discussed from theory and simulation in this paper. The theory relationship between MSG and MDS is given based on our previous work. To verify the linear relation for the point source, we set up an imaging model under atmospheric turbulence. Additionally, the value of MDS will be deviate from that of theory because of the noise of detector and further the deviation will affect the correction effect. The theory results under noise will be obtained through theoretical derivation and then the linear relation between MDS and MDS under noise will be discussed through the imaging model. Results show the linear relation between MDS and MDS under noise is also maintained well, which provides a theoretical support to applications of the model-based WFSless system.
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Huizhen Yang, Huizhen Yang, Liang Ma, Liang Ma, Bin Wang, Bin Wang, } "Linear dependence between the wavefront gradient and the masked intensity for the point source with a CCD sensor", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106201I (12 January 2018); doi: 10.1117/12.2295277; https://doi.org/10.1117/12.2295277
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