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Photonics
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Challenges and Future Directions in Adaptive Optics Technology
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Challenges and Future Directions in Adaptive Optics Technology

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: 20 March 2025 | Viewed by 8402

Special Issue Editors

Dr. Ping Yang

E-Mail Website
Guest Editor
Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Science, Sichuan, China
Interests: adaptive optics; wavefront sensing; intelligent control algorithm; deep learning; high-energy beam control
Dr. Zeyu Gao

E-Mail Website
Guest Editor
Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Science, Chengdu, China
Interests: adaptive optics; wavefront sensing; laser communication; flow measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce a call for papers for our upcoming Special Issue “Challenges and Future Directions in Adaptive Optics Technology” in Photonics. This is a platform used to explore the recent developments, current practices, and future trends in adaptive optics and related fields. Adaptive optics systems and components have achieved a level of sophistication and simplicity that goes beyond the traditional applications in astronomy and into multiple developments, including biology, medicine, manufacturing, communications, ophthalmology, vision science, microscopy, high-energy beam control, and so on. These developments introduce many exciting possibilities. One distinctive tool is AI-powered adaptive optics technology. However, with various communities pursuing different applications of AO and its novel methods, this technology will face many challenges from technical and engineering aspects.

The Special Issue “Challenges and Future Directions in Adaptive Optics Technology” invites original research and comments that introduce the recent advances in adaptive optics from computational, experimental, theoretical, and numerical perspectives, including (but not limited to):

  • AO systems and component technologies;
  • Sensors, measurements, and instrumentation in AO;
  • Advanced Wavefront sensing, correction, shaping methods;
  • Reconstruction and control algorithms;
  • Sensor-less and actuator-less AO;
  • Machine learning and AI for AO, and AO for AI;
  • The modeling and characterization of AO systems and components;
  • Fourier optics, image, and signal processing techniques for AO;
  • High-energy beam control and beam propagation and control;
  • Imaging through scattering and turbid media;
  • Advanced AO in atmospheric, oceanic, and biomedical optics;
  • Advanced AO in optical metrology, optical communication, and microscopy;
  • Novel applications of AO.

Dr. Ping Yang
Dr. Zeyu Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adaptive optics
  • wavefront sensing
  • wavefront correction
  • wavefront shaping
  • optical metrology
  • optical communication
  • machine learning
  • atmospheric, oceanic or biomedical optics

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Published Papers (8 papers)

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Research

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19 pages, 6482 KiB  
Article
Reinforcement Learning-Based Tracking Control under Stochastic Noise and Unmeasurable State for Tip–Tilt Mirror Systems
by Sicheng Guo, Tao Cheng, Zeyu Gao, Lingxi Kong, Shuai Wang and Ping Yang
Photonics 2024, 11(10), 927; https://doi.org/10.3390/photonics11100927 - 30 Sep 2024
Viewed by 638
Abstract
The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack [...] Read more.
The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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13 pages, 4228 KiB  
Article
Cross-Correlation Algorithm Based on Speeded-Up Robust Features Parallel Acceleration for Shack–Hartmann Wavefront Sensing
by Linxiong Wen, Xiaohan Mei, Yi Tan, Zhiyun Zhang, Fangfang Chai, Jiayao Wu, Shuai Wang and Ping Yang
Photonics 2024, 11(9), 844; https://doi.org/10.3390/photonics11090844 - 5 Sep 2024
Viewed by 566
Abstract
A cross-correlation algorithm to obtain the sub-aperture shifts that occur is a crucial aspect of scene-based SHWS (Shack–Hartmann wavefront sensing). However, when the sub-image is partially absent within the atmosphere, the traditional cross-correlation algorithm can easily obtain the wrong shift results. To overcome [...] Read more.
A cross-correlation algorithm to obtain the sub-aperture shifts that occur is a crucial aspect of scene-based SHWS (Shack–Hartmann wavefront sensing). However, when the sub-image is partially absent within the atmosphere, the traditional cross-correlation algorithm can easily obtain the wrong shift results. To overcome this drawback, we propose an algorithm based on SURFs (speeded-up-robust features) matching. In addition, to meet the speed required by wavefront sensing, CUDA parallel optimization of SURF matching is carried out using a GPU thread execution model and a programming model. The results show that the shift error can be reduced by more than two times, and the parallel algorithm can achieve nearly ten times the acceleration ratio. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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31 pages, 6532 KiB  
Article
A Framework for Iterative Phase Retrieval Technique Integration into Atmospheric Adaptive Optics—Part I: Wavefront Sensing in Strong Scintillations
by Mikhail A. Vorontsov and Ernst Polnau
Photonics 2024, 11(9), 786; https://doi.org/10.3390/photonics11090786 - 23 Aug 2024
Viewed by 616
Abstract
The objective of this study, which is divided into two parts, is twofold: to address long-standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations under strong scintillation conditions via a comparative analysis of several basic scintillation-resistant wavefront sensing (SR-WFS) architectures and iterative [...] Read more.
The objective of this study, which is divided into two parts, is twofold: to address long-standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations under strong scintillation conditions via a comparative analysis of several basic scintillation-resistant wavefront sensing (SR-WFS) architectures and iterative phase retrieval (IPR) techniques (Part I, this paper), and to develop a framework for the potential integration of SR-WFS techniques into practical closed-loop non-astronomical atmospheric adaptive optics (AO) systems (Part II). In this paper, we consider basic SR-WFS mathematical models and phase retrieval algorithms, tradeoffs in sensor design and phase retrieval technique implementation, and methodologies for WFS parameter optimization and performance assessment. The analysis is based on wave-optics numerical simulations imitating realistic turbulence-induced phase aberrations and intensity scintillations, as well as optical field propagation inside the SR-WFSs. Several potential issues important for the practical implementation of SR-WFS and IPR techniques, such as the requirements for phase retrieval computational grid resolution, tolerance with respect to optical element misalignments, and the impact of camera noise and input light non-monochromaticity, are also considered. The results demonstrate that major wavefront sensing requirements desirable for AO operation under strong intensity scintillations can potentially be achieved by transitioning to novel SR-WFS architectures, based on iterative phase retrieval techniques. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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16 pages, 14680 KiB  
Article
Adaptive Image-Defogging Algorithm Based on Bright-Field Region Detection
by Yue Wang, Fengying Yue, Jiaxin Duan, Haifeng Zhang, Xiaodong Song, Jiawei Dong, Jiaxin Zeng and Sidong Cui
Photonics 2024, 11(8), 718; https://doi.org/10.3390/photonics11080718 - 31 Jul 2024
Viewed by 893
Abstract
Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with the dark-channel prior being the most frequently employed. However, halo [...] Read more.
Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with the dark-channel prior being the most frequently employed. However, halo and bright-field color distortion issues persist. To further improve image quality, an adaptive image-defogging algorithm based on bright-field region detection is proposed in this paper. Modifying the dark-channel image improves the abrupt changes in gray value in the traditional dark-channel image. By setting the first and second lower limits of transmittance and introducing an adaptive correction factor to adjust the transmittance of the bright-field region, the limitations of the dark-channel prior in extensive ranges and high-brightness areas can be significantly alleviated. In addition, a guide filter is utilized to enhance the initial transmittance image, preserving the details of the defogged image. The results of the experiment demonstrate that the algorithm presented in this paper effectively addresses the mentioned issues and has shown outstanding performance in both objective evaluation and subjective visual effects. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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19 pages, 68986 KiB  
Article
Flow Field Estimation with Distortion Correction Based on Multiple Input Deep Convolutional Neural Networks and Hartmann–Shack Wavefront Sensing
by Zeyu Gao, Xinlan Ge, Licheng Zhu, Shiqing Ma, Ao Li, Lars Büttner, Jürgen Czarske and Ping Yang
Photonics 2024, 11(5), 452; https://doi.org/10.3390/photonics11050452 - 11 May 2024
Viewed by 1435
Abstract
The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, [...] Read more.
The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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15 pages, 5457 KiB  
Article
Improved DeepLabV3+ Network Beacon Spot Capture Methods
by Jun Liu, Xiaolong Ni, Xin Yu and Cong Li
Photonics 2024, 11(5), 451; https://doi.org/10.3390/photonics11050451 - 11 May 2024
Cited by 1 | Viewed by 891
Abstract
In long-range laser communication, adaptive optics tracking systems are often used to achieve high-precision tracking. When recognizing beacon spots for tracking, the traditional threshold segmentation method is highly susceptible to segmentation errors in the face of interference. In this study, an improved DeepLabV3+ [...] Read more.
In long-range laser communication, adaptive optics tracking systems are often used to achieve high-precision tracking. When recognizing beacon spots for tracking, the traditional threshold segmentation method is highly susceptible to segmentation errors in the face of interference. In this study, an improved DeepLabV3+ network is designed for fast and accurate capture of beacon spots in complex situations. In order to speed up the inference process, the backbone of the model was rewritten as MobileNetV2. This study improves the ASPP (Atrous Spatial Pyramid Pooling) module by splicing and fusing the outputs and inputs of its different layers. Meanwhile, the original convolution in the module is rewritten as a depthwise separable convolution with a dilation rate to reduce the computational burden. CBAM (Convolutional Block Attention Module) is applied, and the focus loss function is introduced during training. The network yields an accuracy of 98.76% mean intersection over union on self-constructed beacon spot dataset, and the segmentation consumes only 12 milliseconds, which realizes the fast and high-precision capturing of beacon spots. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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10 pages, 1173 KiB  
Article
Compressed Sensing Image Reconstruction with Fast Convolution Filtering
by Runbo Guo and Hao Zhang
Photonics 2024, 11(4), 323; https://doi.org/10.3390/photonics11040323 - 30 Mar 2024
Cited by 1 | Viewed by 1160
Abstract
Image reconstruction is a crucial aspect of computational imaging. The compressed sensing reconstruction (CS) method has been developed to obtain high-quality images. However, the CS method is commonly time-consuming in image reconstruction. To overcome this drawback, we propose a compressed sensing reconstruction method [...] Read more.
Image reconstruction is a crucial aspect of computational imaging. The compressed sensing reconstruction (CS) method has been developed to obtain high-quality images. However, the CS method is commonly time-consuming in image reconstruction. To overcome this drawback, we propose a compressed sensing reconstruction method with fast convolution filtering (F-CS method), which significantly increases reconstruction speed by reducing the number of convolution operations without image fill. The experimental results show that by using the F-CS method, the reconstruction speed can be increased by a factor of 7 compared to the conventional CS method. Moreover, the F-CS method proposed in this paper is compared with the back-propagation reconstruction (BP) method and super-resolution reconstruction (SR) method, and it is validated that the proposed method has a lower computational resource cost for high-quality image reconstruction and exhibits a much more balanced capability. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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Review

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23 pages, 19563 KiB  
Review
A Review: Phase Measurement Techniques Based on Metasurfaces
by Zhicheng Zhao, Yueqiang Hu and Shanyong Chen
Photonics 2024, 11(11), 996; https://doi.org/10.3390/photonics11110996 - 22 Oct 2024
Viewed by 1187
Abstract
Phase carries crucial information about the light propagation process, and the visualization and quantitative measurement of phase have important applications, ranging from ultra-precision metrology to biomedical imaging. Traditional phase measurement techniques typically require large and complex optical systems, limiting their applicability in various [...] Read more.
Phase carries crucial information about the light propagation process, and the visualization and quantitative measurement of phase have important applications, ranging from ultra-precision metrology to biomedical imaging. Traditional phase measurement techniques typically require large and complex optical systems, limiting their applicability in various scenarios. Optical metasurfaces, as flat optical elements, offer a novel approach to phase measurement by manipulating light at the nanoscale through light-matter interactions. Metasurfaces are advantageous due to their lightweight, multifunctional, and easy-to-integrate nature, providing new possibilities for simplifying traditional phase measurement methods. This review categorizes phase measurement techniques into quantitative and non-quantitative methods and reviews the advancements in metasurface-based phase measurement technologies. Detailed discussions are provided on several methods, including vortex phase contrast, holographic interferometry, shearing interferometry, the Transport of Intensity Equation (TIE), and wavefront sensing. The advantages and limitations of metasurfaces in phase measurement are highlighted, and future research directions are explored. Full article
(This article belongs to the Special Issue Challenges and Future Directions in Adaptive Optics Technology)
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