Adaptive optics is widely used to correct aberrations in the human eye, achieving high-resolution imaging of fundus cells and microvessels. Traditional adaptive optics systems are limited by the dynamic range of the Shack Hartmann wavefront detector and are only suitable for some people, and cannot perform fundus high-resolution imaging in people with high refractive errors. In order to improve the universality of fundus adaptive optical imaging system, this paper designs a high-resolution fundus adaptive optical imaging system based on voice coil deformation mirror. The introduction of the Badal focusing system can perform high-resolution imaging of the fundus with the diopter of the human eye between -8 similar to 8 D. In this system, the traditional annular diaphragm is replaced with an adjustable axial cone lens set, the spacing of positive and negative axonal cone lenses are controlled to adjust the inner diameter of annular light in order to adapt the cornea of different eyes, and avoid stray light reflected by the cornea. Large field of view imaging is realized through visual beacon guidance. The simulation results show that the illumination subsystem has a uniform distribution of retinal illumination in the fundus. Within the set tolerance, at least 90% of the MTF values reach 0. 21 at 25 lp/mm (corresponding to 4 mu m on the retina). The corresponding optical path was built in the laboratory, and the simulated human eye with large distortion was imaged, and a good imaging effect was obtained.

When the compressive sensoring is used in wavefront measurement,classic methods of slopes' restoration has a relatively low precision,which make it difficult to measure the atmospheric turbulence wavefront. In the paper,a deep neural network is presented to improve the slopes'restoration precision. The traditional compressive sensing technology does not take into account the relatively small slopes,which increases the wavefront measurement errors. To measure the complex wavefront induced by atmospheric turbulence with a high speed,the paper presents an improved deep neural network to restore the slopes from sparse ones with high precision,which improves the precision of wavefront reconstruction. When the compression ratio is ranged from 0. 1 to 0. 9,the wavefront error PV (Peak to valley) of the compressed wavefront detection algorithm based on depth neural network(DNNCWS)proposed in this paper is better than 0. 014 mu m,and the running time of the algorithm is 4. 4 ms. In the case of low signal-to-noise ratio, the residual wavefront PV is better than 0. 011 mu m. In addition,the simulation results indicate that it has good anti-noise performance. The DNNCWS improves the detection accuracy of compressive sensing and overcomes the problem of low accuracy for complex aberration induced by atmospheric turbulence. It can also be used in other adaptive optical applications,such as laser communication and retinal imaging.

The dynamically changing atmospheric turbulence and the reduced brightness of the observed target severely affect the accuracy of the Shack-Hartmann wavefront sensor(SHWFS)to detect wavefronts. Under these two complicated observational conditions,this paper proposes a neural network model based on Transformer structure,which has excellent global modelling capabilities and could reconstruct wavefronts from light spot array images from SHWFS with high accuracy. The residual wavefront RMS error of the presented network model can be stabilized between 0. 010 mu m and 0. 024 mu m by simulating for dynamically varying typical atmospheric turbulence coherence length r0. Comparing with reported methods, the wavefront aberrations can be reconstructed more accurately. In addition,the reconstruction accuracy of the method is robust to the magnitude variation of guide stars or detection targets. Therefore,the reconstruction accuracy of this method has strong stability to the changes of two observation conditions,and provides a promising way for high-resolution imaging for large- aperture astronomical optical telescopes.