CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13153 > Article
Paper
30 April 2024 Point source wavefront reconstruction using Swin-UNet in sensorless adaptive optics systems with CCD light intensity images
Qingyang Zhang, Heng Zuo, Xiangqun Cui, Xiangyan Yuan, Tianzhu Hu
Author Affiliations +
Proceedings Volume 13153, Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems; 1315319 (2024) https://doi.org/10.1117/12.3018860
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China
Abstract
Adaptive optics (AO) systems are trending towards miniaturization and cost reduction, with wavefront sensorless adaptive optics systems (WFSless AOSs) emerging as a field of interest due to their simple structure and application versatility. The advent of deep learning has propelled the use of convolutional neural networks (CNNs) to extract aberration information from CCD images. Nevertheless, CNNs often fail to focus on the regions of images where effective information is concentrated, which limits the accuracy in aberration extraction. This paper introduces a novel Swin-UNet-based model for WFSless AOSs based on point source that employs an attention mechanism to target relevant areas within CCD light intensity images, thus addressing CNN shortcomings. Furthermore, the proposed model fuses in-focus and out-of- focus image information to directly output the reconstructed wavefront image, enhancing the overall wavefront reconstruction process. Our simulations across various D/r0 ratios reveal significant improvements with the Swin-UNet-based model: a reduction in rms wavefront error from 0.0219 to 0.0061 wavelengths at D/r0=1, from 0.0806 to 0.03825 at D/r0=6, and from 0.1241 to 0.0991 at D/r0=11. Correspondingly, the Strehl ratio improved from 0.9950 to 0.9988, from 0.8380 to 0.9567, and from 0.6814 to 0.7522, indicating enhanced image quality post-correction. Compared with existing CNN-based technology, our Swin-UNet approach can more effectively concentrate on the relevant areas of the image and mitigate the influence of invalid regions on the analysis, thereby substantially improving the effectiveness and robustness of the correction performance in WFSless AOSs.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Qingyang Zhang, Heng Zuo, Xiangqun Cui, Xiangyan Yuan, and Tianzhu Hu "Point source wavefront reconstruction using Swin-UNet in sensorless adaptive optics systems with CCD light intensity images", Proc. SPIE 13153, Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems, 1315319 (30 April 2024); https://doi.org/10.1117/12.3018860
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 12 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Adaptive optics
Wavefronts
Data modeling
Education and training
Performance modeling
Deep learning
Charge-coupled devices
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top