Improving wavefront reconstruction performance with a binary phase hybrid Shack-Hartmann wavefront sensor.

The Shack-Hartmann wavefront sensor's wavefront reconstruction performance can be improved if more wavefront details over sub-apertures can be acquired. Based on this idea, we design a kind of binary phase hybrid Shack-Hartmann wavefront sensor (BPH-SHWFS) with binary phase modulation in each sub-aperture. After modulation, it's easier to extract high-order aberration modes for each sub-aperture by a specially designed neural network. We modified the reconstruction matrix with more modes' information, and then the wavefront reconstruction accuracy and resolution were improved. A series of experiments are conducted to verify the validity of the BPH-SHWFS. Experimental results have shown that the proposed sensor can accurately sense higher-order wavefront information compared to the conventional Shack-Hartmann wavefront sensor (C-SHWFS) with the same spatial sampling rate. In the case of strong atmospheric turbulence, the BPH-SHWFS' spatial resolution is able to break the C-SHWFS' limit and reconstruct the aberration precisely, even with sparse sub-apertures. Besides, the well-trained network is for a single sub-aperture, so once the training process is complete, the network still has universality after the number of sub-apertures changes.