基于深度学习的自适应光学技术研究进展及展望

Research Progress and Prospect of Adaptive Optics Based on Deep Learning

自适应光学是一种校正波前误差的技术,在地基望远镜、生物成像、人眼像差校正、激光通信等领域中已经有了广泛的应用。与此同时,深度学习技术的快速发展为各个领域带来了全新的方法。为了进一步提升传统自适应光学系统的性能,研究者将自适应光学技术与深度学习相结合,从实时性、抗噪声干扰能力等角度对已有自适应光学系统进行了改进。首先对目前常用的人工神经网络架构进行了介绍,然后详细阐述了近五年深度学习与自适应光学技术相结合的方法,最后对已有方法进行了总结,并对该技术未来的发展方向进行了展望。

Significance Adaptive optics(AO)technology enhances imaging quality by measuring and compensating for wavefront errors.It has been widely used in ground-based telescopes,biological imaging,ocular aberration correction,and laser communication,and so on.Current AO systems can be categorized into two groups depending on the presence or absence of a wavefront sensor(WFS).Wavefront sensorless(WFSless)AO technology acquires the pupil phase via a retrieval algorithm based on the light intensity distribution.This type of technology can be divided into two kinds:single-image-based and phase-diversity-based technology.Single image-based technology measures the wavefront errors through a single intensity image.However,the phase distribution obtained from a solitary intensity image follows a one-to-many mapping relationship,resulting in limited accuracy.On the other hand,the phase-diversity-based AO technique can determine the phase distribution of the optical field on the input plane by collecting image information of the focal plane and the defocusing plane,resulting in a higher detection accuracy.However,a large number of iterations and measurements are required to obtain optimal results using traditional WFSless AO technology,making it unsuitable for high-speed and real-time scenarios.WFS AO technology employs a WFS based on the interference principle or a traditional geometric optics principle to measure the wavefront.Examples of WFSs used include phase-shifting interference WFSs,Shack-Hartmann WFSs(SHWFSs),and pyramid WFSs(PyWFSs).A high measurement accuracy is achieved using the traditional phaseshifting interference WFS method,but its real-time performance is suboptimal and is susceptible to environmental disturbances.The SHWFS is widely used in AO systems due to its simple structure and ease of operation.However,as a result of its pupil segmentation mechanism,the spatial resolution of the image is low and the dynamic range is small.While the PyWFS can detect weaker light than SHWFS AO technology,it is expensive and has a small linear range in the unmodulated mode.Recently,the rapid development of artificial intelligence has accelerated development in various fields.Deep learning technology,a significant branch of artificial intelligence,has exhibited remarkable capabilities in search,data mining,machine translation,and speech recognition.Deep learning algorithms are founded on artificial neural networks,which optimize weights and biases based on the given sets of samples.The neural network,after being trained with vast amounts of data,can accurately establish the input-output relationship.Despite the prolonged training time,results can be inferred quickly,making it useful in a multitude of technical domains.The combination of AO and deep learning technology is expected to overcome the issues encountered in conventional AO techniques.It is hypothesized that deep learning can lead to faster and more precise wavefront correction,thereby enhancing the performance of AO technology.Progress This review introduces several popular artificial neural networks(Fig.1)used in deep learning.The ways in which deep learning has been combined with AO technology are classified into two categories:techniques with and techniques without WFS.The WFSless category is subdivided into single-image-based(Figs.3‒4)and phase-diversity-based(Figs.5‒6)technologies,while the WFS category includes examples of SHWFSs(Figs.7‒9)and other WFS technologies combined with deep learning.Moreover,the review introduces a new diffraction neural network(Fig.10),building on the traditional neural network,and provides examples of how this diffraction neural network has been combined with AO technology.The review notes that,over the past five years,examples of deep learning combined with AO technology have focused on improving the real-time performance and accuracy of traditional AO techniques.Finally,the review discusses the future development directions for deep learning-based AO technology.Conclusions and Prospects Utilizing deep learning with WFSless AO technology provides several favorable advantages,such as its simple structure and low cost.While the single-image-based method only uses one image to correct the aberration,the corresponding phase of the intensity image reveals a one-to-many mapping,ultimately resulting in inaccurate calculations.On the other hand,the phase-diversity-based method uses two images with known phase differences to determine the size of the aberrations,yielding more accurate results than via the single-image-based method.Within the WFS AO technology field,numerous SHWFSbased methods exist.With a focus on improving the accuracies of centroid position and wavefront reconstruction,the application of deep learning networks has accelerated and further improved accuracy.Wavefront measurement methods based on sensors other than the SHWFS have gradually been integrated with deep-learning technology.In the future,deep learning algorithms will be combined with other technologies,including reinforcement learning,and applied to other types of sensors such as the PyWFS to further enhance AO performance.Furthermore,AO will likely be integrated with a novel optical neural network to optimize its performance.Despite the growing body of literature on deep-learning based AO,most studies have been limited to simulation data;thus,it is imperative to evaluate deep-learning based AO using real-world scenarios.Moreover,while current AO technology focuses on the correction of point-source wavefront errors,the detection of extended-source wavefront errors should also be explored in future developments.