WFSless Adaptive Optics Research Articles

Research progress in deep learning based WFSless adaptive optics system

In recent years, Adaptive Optics (AO) system is developing towards miniaturization and low cost. Because of its simple structure and wide application range, wavefront sensorless (WFSless) AO system has become a research hotspot in related fields. Under the condition that the hardware environment is determined, the system control algorithm determines the correction effect and convergence speed of WFSless AO system. The emerging deep learning and artificial neural network have injected new vitality into the control algorithms of WFSless AO system, and further promoted the theoretical and practical development of WFSless AO. On the basis of summarizing the previous control algorithms of WFSless AO system, the applications of convolution neural network (CNN), long-term memory neural network (LSTM) and deep reinforcement learning in WFSless AO system control in recent years are comprehensively introduced, and characteristics of various deep learning models in WFSless AO system are summarized. Applications of WFSless AO system in astronomical observation, microscopy, ophthalmoscopy, laser telecommunication and other fields are outlined.

Wavefront sensorless adaptive optics system for extended objects based on linear phase diversity technique

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. However, most of the existing WFSless AO systems require point sources for wavefront sensing. Aimed to correct the turbulence-degraded imaging of extended objects, this document proposes a fast wavefront correction approach for a WFSless AO system based on linear phase diversity (PD) technique. By linearizing the system optical transfer function (OTF) and expanding the influence functions of the deformable mirror (DM) on Zernike basis, this method can establish a linear relationship model between the drive voltages of the DM and the CCD acquired extended images. Then the control signal of the DM can be directly output to generate a compensated wavefront from CCD images and fast adaptive wavefront correction can be realized. Numerical simulations and an experimental validation are performed under different turbulence strengths, the results show that the proposed method has an impressive improvement of root mean square (RMS) of wavefront and can effectively restore the turbulence-degraded extended images with a relatively fast correction speed.

Large Aberration Correction by Magnetic Fluid Deformable Mirror with Model-Based Wavefront Sensorless Control Algorithm.

Magnetic fluid is a stable colloidal suspension of nano-sized, single-domain ferri/ferromagnetic particles dispersed in a liquid carrier. The liquid can be magnetized by the ferromagnetic particles aligned with the external magnetic field, which can be used as a wavefront corrector to correct the large aberrations up to more than 100 µm in adaptive optics (AO) systems. Since the measuring range of the wavefront sensor is normally small, the application of the magnetic fluid deformable mirror (MFDM) is limited with the WFS based AO system. In this paper, based on the MFDM model and the relationship between the second moment (SM) of the aberration gradients and the far-field intensity distribution, a model-based wavefront sensorless (WFSless) control algorithm is proposed for the MFDM. The correction performance of MFDM using the model-based control algorithm is evaluated in a WFSless AO system setup with a prototype MFDM, where a laser beam with unknown aberrations is supposed to produce a focused spot on the CCD. Experimental results show that the MFDM can be used to effectively compensate for unknown aberrations in the imaging system with the proposed model-based control algorithm.

Aberration correction based on wavefront sensorless adaptive optics in membrane diffractive optical telescope

The membrane diffractive optical telescopes are one of areas of active research of large aperture optical telescopes. However, due to the processing technology of thin membrane mirror, the changes of temperature and humidity, etc., the imaging performance of membrane diffractive optical telescope is degraded sharply and it is difficult to reach the design target. Adaptive optics (AO) technology is a potential solution to improve the imaging performance of this telescope. Our work is the first time to correct system aberration of hundred millimeter-level aperture membrane diffractive optical telescope based on AO technology. The point target and extended target imaging experiment of the telescope with WFSless AO system in the laboratory are accomplished. The experimental results demonstrate the effectiveness of AO correction in improving the imaging performance of membrane diffractive optical telescope. Especially when there are unexpected aberrations introduced in the telescope, such as disturbances caused by assembly and operation, AO correction will be an important guarantee for the telescope to maintain good imaging performance.

DNN-based aberration correction in a wavefront sensorless adaptive optics system.

Existing wavefront sensorless (WFS-less) adaptive optics (AO) generally require a search algorithm that takes lots of iterations and measurements to get optimal results. So the latency is a serious problem in the current WFS-less AO system, especially in applications to free-space optics communication. To solve this issue, we propose a deep neural network (DNN)-based aberration correction method. The DNN model can detect the wavefront distortion directly from the intensity images, thereby avoiding time-consuming iterative processes. Since the tip-and-tilt mode of Zernike coefficients are considered, the tip-tilt correction system is not necessarily required in the proposed method. From our simulation results, the proposed method can effectively reduce the computation time and has an impressive improvement of root mean square (RMS) in different turbulence conditions.

Fast correction approach for wavefront sensorless adaptive optics based on a linear phase diversity technique.

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. To reach optimum results, such systems require an efficient correction method. This paper presents a fast wavefront correction approach for a WFSless AO system mainly based on the linear phase diversity (PD) technique. The fast closed-loop control algorithm is set up based on the linear relationship between the drive voltage of the deformable mirror (DM) and the far-field images of the system, which is obtained through the linear PD algorithm combined with the influence function of the DM. A large number of phase screens under different turbulence strengths are simulated to test the performance of the proposed method. The numerical simulation results show that the method has fast convergence rate and strong correction ability, a few correction times can achieve good correction results, and can effectively improve the imaging quality of the system while needing fewer measurements of CCD data.

Model-based wavefront sensorless adaptive optics system for large aberrations and extended objects.

A model-based wavefront sensorless (WFSless) adaptive optics (AO) system with a 61-element deformable mirror is simulated to correct the imaging of a turbulence-degraded extended object. A fast closed-loop control algorithm, which is based on the linear relation between the mean square of the aberration gradients and the second moment of the image intensity distribution, is used to generate the control signals for the actuators of the deformable mirror (DM). The restoration capability and the convergence rate of the AO system are investigated with different turbulence strength wave-front aberrations. Simulation results show the model-based WFSless AO system can restore those images degraded by different turbulence strengths successfully and obtain the correction very close to the achievable capability of the given DM. Compared with the ideal correction of 61-element DM, the averaged relative error of RMS value is 6%. The convergence rate of AO system is independent of the turbulence strength and only depends on the number of actuators of DM.

Model-based aberration correction in a closed-loop wavefront-sensor-less adaptive optics system

In many scientific and medical applications, such as laser systems and microscopes, wavefront-sensor-less (WFSless) adaptive optics (AO) systems are used to improve the laser beam quality or the image resolution by correcting the wavefront aberration in the optical path. The lack of direct wavefront measurement in WFSless AO systems imposes a challenge to achieve efficient aberration correction. This paper presents an aberration correction approach for WFSlss AO systems based on the model of the WFSless AO system and a small number of intensity measurements, where the model is identified from the input-output data of the WFSless AO system by black-box identification. This approach is validated in an experimental setup with 20 static aberrations having Kolmogorov spatial distributions. By correcting N=9 Zernike modes (N is the number of aberration modes), an intensity improvement from 49% of the maximum value to 89% has been achieved in average based on N+5=14 intensity measurements. With the worst initial intensity, an improvement from 17% of the maximum value to 86% has been achieved based on N+4=13 intensity measurements.