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Photonics
Special Issues
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: 10 September 2024 | Viewed by 1620

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, Sichuan, China
Interests: adaptive optics; wavefront sensing; laser communication; flow measurements

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

Published Papers (3 papers)

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Research

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 375
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
Viewed by 299
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
Viewed by 620
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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