Mr. Hao Wang Profile

Hao Wang

at Beijing Institute of Technology

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 April 2023 Paper

Tip-tilt error detection for segments based on CNN

Xiang Li, Weirui Zhao, Hao Wang, Lu Zhang

Proceedings Volume 12617, 1261752 (2023) https://doi.org/10.1117/12.2666436

KEYWORDS: Mirrors, Error analysis, Image segmentation, Point spread functions, Education and training, Signal to noise ratio, Imaging systems, CCD cameras, Technology, Histograms

Read Abstract +

The segmented primary mirror telescope under the co-phasing condition can meet the observation requirements of high resolution. However, co-phase errors are always present, which seriously affects the imaging quality. The precise phase modulation requires that the root mean square error of wavefront is less than 𝜆 ⁄ 40. Therefore, the high-precision detection of tip-tilt error between the segments is one of the key technologies to realize the co-phase imaging. In this paper, we propose a simple and efficient tip-tilt error detection method based on single Convolution Neural Network (CNN). Without any preprocessing, the light intensity distribution images on the focal plane are used as the data set for training CNN. And a high-performance CNN model is built to learn the mapping between the tip-tilt errors and light intensity distribution images. After training, CNN can accurately capture the tip-tilt errors by inputting a single image of the light intensity distribution. The simulation model of a three-segment telescope system is established to test the accuracy and robustness of the method. Test results show that the method can achieve high-precision detection of tip-tilt error in a large detection range. This method can achieve a detection range of [-3𝜆,3𝜆] with an accuracy of 7.820×10-3𝜆RMS. The method is robust to the piston error and CCD noise: the tolerance of CCD noise is 5 dB and the tolerance of piston error is [-0.48 𝜆,0.48 𝜆]. This method is simple and does not require complex hardware. It can be widely applied in segmented and deployable primary mirror telescopes.

Proceedings Article | 8 July 2022 Paper

A piston detection method for segments via convolutional neural networks and its robustness analysis

Hao Wang, Gang Liu, Weirui Zhao, Lu Zhang, Yuejin Zhao

Proceedings Volume 12282, 122821L (2022) https://doi.org/10.1117/12.2620627

KEYWORDS: Point spread functions, Mirrors, Image segmentation, Telescopes, Signal to noise ratio, Charge-coupled devices, Error analysis, Tolerancing, Segmented mirrors, Light sources

Read Abstract +

To achieve a diffraction-limited imaging, the piston errors between the segments of the segmented primary mirror telescope should be reduced to λ/40 RMS. The piston detection method using convolutional neural network (CNN) is an advanced technology with high precision and simplicity. However, such methods based on the deep learning strategy usually have generalization problems, that is, the network prediction precision will inevitably decrease if there is a certain difference between the test image and training set used in the network. This will directly affect the scope of application of the method. In this letter, we propose a CNN-based high-precision piston detection method and analyze its robustness. The point spread function (PSF) images acquired under the wide-spectrum light source are used to construct the dataset to overcome 2π ambiguity. In addition, a set of neural networks system including the classification CNN and the regression CNN with good generalization ability is designed to extract the piston value directly from the PSF image. Under the ideal condition, the piston detection precision can reach about 8.4 X 10^-4 λοRMS in the capture range of the interference length of the operating light. Finally, we focus on testing the effect degree of the main disturbance factors in the actual system on the accuracy of the method, such as surface error, residual tip-tilt error, and CCD noise, so as to evaluate the robustness of the method. This method is robust and does not require complex hardware. It can be widely applied in segmented and deployable primary mirror telescopes. We believe that the study in this letter will contribute to the applications of the CNN-based technique for piston sensing.

Proceedings Article | 12 March 2021 Paper

High-precision piston detection method for segments based on a single convolutional neural network

Hao Wang, Weirui Zhao, Lu Zhang

Proceedings Volume 11763, 1176391 (2021) https://doi.org/10.1117/12.2587641

Read Abstract +

High-precision detection of piston error is one of the key technologies for high-resolution large-aperture segmented telescopes. Most piston detection methods based on neural networks are difficult to achieve high accuracy. In this Letter, we propose a high-precision piston error detection method based on convolutional neural networks (CNN). A system with six sub-mirrors is used, and one of the sub-mirrors is set as the reference mirror. The network can simultaneously extract the piston information of the remaining five sub-mirrors to be tested from the point spread function (PSF). In the training phase, five sub-mirrors are set with 10,000 groups of random piston values with a range slightly less than one wavelength, and PSF images can be acquired accordingly. Then, 10,000 PSF images with corresponding piston errors are used to train the network. After training, we only need to input a PSF image into the pre-trained network, and the piston can be obtained directly. It is verified by simulation that the average piston’s measurement error of five submirrors is just 0.0089λ RMS (λ=632nm). In addition, this end-to-end method based on deep learning extremely reduces the complexity of the optical system, and just need to set a mask with a sparse multi-subaperture configuration in the conjugate plane of the segmented mirror. This method is accurate and fast, and can be widely used to detect the piston in phasing telescope arrays or segmented mirrors.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years