Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Ge, Xinlan; Zhu, Licheng; Gao, Zeyu; Wang, Ning; Zhao, Wang; Ye, Hongwei; Wang, Shuai

doi:10.3788/col202321.060101

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…From the references Meynadier et al (1999) and Ge et al (2023), to obtain accurate wavefront sensing results, the peakto-valley value of the defocus aberration is typically within the range of 1λ ± λ/2. In this work, the fixed defocus amount δ is set to 1λ.…”

Section: Multi-object Wavefront Coupling Dataset (Mowc-mentioning

confidence: 99%

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

Ge,

Zhu,

Gao

et al. 2024

ApJL

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This Letter proposes a universal wavefront reconstruction approach based on a coupled data set and neural network, aiming to overcome the limitations of current algorithms in terms of universality and wavefront sensing accuracy for variable imaging objects. First, a novel data set, Multi-Object Wavefront Coupling Dataset (MOCD-Dataset), is developed to provide diverse data and enable the network to learn universal wavefront features. Next, a new universal wavefront reconstruction network called Object-Independent Wavefront Decoupling Network (OIWD-Net) is introduced, aiming to separate imaging object information from multiple variable images. Our algorithm eliminates the need for specialized wavefront sensors, has a simple system, high light energy utilization, and does not require customized models for each different type of imaging objects, making it highly practical. By combining the MOCD-Dataset and the OIWD-Net, excellent accuracy in wavefront reconstruction of different imaging objects has been achieved. This research provides a new solution for high-resolution image restoration in fields such as solar structure observation and astronomical high-resolution imaging.

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Section: Multi-object Wavefront Coupling Dataset (Mowc-mentioning

confidence: 99%

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

Ge,

Zhu,

Gao

et al. 2024

ApJL

Self Cite

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Experimental demonstration of wavefront reconstruction and correction techniques for variable targets based on distorted grating and deep learning

Ge,

Zhu,

Gao

et al. 2024

Opt. Express

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This research presents a practical approach for wavefront reconstruction and correction adaptable to variable targets, with the aim of constructing a high-precision, general extended target adaptive optical system. Firstly, we delve into the detailed design of a crucial component, the distorted grating, simplifying the optical system implementation while circumventing potential issues in traditional phase difference-based collection methods. Subsequently, normalized fine features (NFFs) and structure focus features (SFFs) which both are independent of the imaging target but corresponded precisely to the wavefront aberration are proposed. The two features provide a more accurate and robust characterization of the wavefront aberrations. Then, a Noise-to-Denoised Generative Adversarial Network (N2D-GAN) is employed for denoising real images. And a lightweight network, Attention Mechanism-based Efficient Network (AM-EffNet), is applied to achieve efficient and high-precision mapping between features and wavefronts. A prototype of object-independent adaptive optics system is demonstrated by experimental setup, and the effectiveness of this method in wavefront reconstruction for different imaging targets has been verified. This research holds significant relevance for engineering applications of adaptive optics, providing robust support for addressing challenges within practical systems.

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Arbitrary field-of-view wavefront monitoring method based on an improved vision transformer network

Liu,

Ju,

et al. 2024

Opt. Express

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Space-based segmented telescopes are susceptible to mirror misalignments because of temperature and mechanical perturbations in orbit. Monitoring the misalignment status of each mirror is a prerequisite to aligning the telescope promptly and maintaining image quality. In this paper, an online monitoring method based on an improved vision transformer network is proposed. Two new modules have been added to the method: the grid-aligned patch-splitting (GAPS) module and the field linearization eliminator (FLE) module. The former modifies the patch-splitting method based on the characteristics of point spread function (PSF) to improve the accuracy. The latter is used to eliminate the effect of field position for arbitrary field-of-view wavefront monitoring. The method predicts the misalignment state of the telescope through a PSF (stellar image) for an arbitrary field of view, realizing monitoring of the telescope without hindering the observing process and increasing the complexity of the system. Simulations and experiments are performed to verify the effectiveness and practicality of the method.

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Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

Cited by 4 publications

References 21 publications

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

Experimental demonstration of wavefront reconstruction and correction techniques for variable targets based on distorted grating and deep learning

Arbitrary field-of-view wavefront monitoring method based on an improved vision transformer network

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