Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (2): 543-554.doi: 10.13229/j.cnki.jdxbgxb.20240835

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High resolution computational imaging technology for a simple adaptive optics system

Dan YUE1(),Chong-shuai WANG1,Ya-ting YANG1,Hai-tao NIE2(),Ya-hui CHUAI1   

  1. 1.College of Physics，Changchun University of Science and Technology，Changchun 130022，China
    2.Changchun Institute of Optics，Precision Mechanics and Physics，Chinese Academy of Sciences，Changchun 130033，China
  • Received:2024-07-24 Online:2026-02-01 Published:2026-03-17
  • Contact: Hai-tao NIE E-mail:yuedan@cust.edu.cn;kelek2@126.com

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Abstract:

Aimed at the problems of current hardware-based adaptive optics including incomplete wavefront correction， high hardware cost， and complexity of the system structure， this paper proposed a new simple adaptive optics to achieve high resolution imaging of the observed target. It completely abandoned the traditional wavefront detection and correction devices， while used computational imaging technology to directly correct the wavefront aberrations at the image level through deep learning algorithm. Firstly， based on deep learning algorithm， a system focal plane degraded image is used to calculate atmospheric turbulence aberrations once and for all. Then based on the resolved turbulence aberrations， a deconvolution is processed to the degraded image to obtain high-resolution reconstruction of the observed object. The simulation results show that the proposed deep neural network can solve the atmospheric turbulence aberrations with high accuracy and high speed under the configured hardware environment. The quality of the image recovered by the deconvolution strategy based on the solved aberrations is greatly improved compared with the degraded image without correction， and the high-resolution imaging for the proposed simple adaptive optics without hardware is realized

Key words: adaptive optics, simple imaging system, computational imaging, deep learning

CLC Number: 

  • O436

Fig.1

Diagram of the simple adaptive optics system model"

Fig.2

General flow chart of the proposed algorithm"

Fig.3

EfficientNet-B0 network structure"

Fig.4

SepConv module diagram"

Fig.5

MBConvBlock module diagram"

Fig.6

Data set generation process"

Table 1

Training parameters of EfficientNet-B0 network"

CNN网络输入Epochs优化函数学习率批处理数损失函数
EfficientNet-B0224×224300Adam0.000 0564MSE

Fig.7

Loss functions of EfficientNet-B0 network"

Fig.8

One set of Zernike coefficients prediction results under four turbulence intensities"

Fig.9

Turbulent wavefront corresponding to the predicted Zernike coefficients under the four turbulence intensitie"

Fig.10

Statistical results of the wavefront predictions for the constructed 2，000 test data sets by EfficientNet-B0 network"

Fig.11

Average wavefront detection accuracy of different networks under four turbulence intensities"

Table 2

Image recovery effect of observation target “satellite” under four turbulence intensities"

原始图像原始波前退化图像预测波前

复原图像

（C=0.01）

复原图像

（C=0.000 1）

复原图像

（C=0.000 01）

Table 3

Specific values of the image recovery evalua-tion indexes for the observation target “satellite“ under the four turbulence intensities"

大气湍流D/r0退化图像

功率

谱比C

复原图像
MSEPSNR/dBMSEPSNR/dB
5240.9824.310.0118.6135.43
0.000 10.6050.34
0.000 010.6250.21
10364.2322.520.0141.1132.00
0.000 114.6036.49
0.000 0116.6135.93
15542.5320.790.01124.2427.19
0.000 144.1931.68
0.000 0173.3129.48
20721.6417.180.01173.6822.21
0.000 158.3426.37
0.000 0179.5725.08

Table 4

Image recovery effect of observation target "resolution plate" under four turbulence intensities"

原始图像原始波前退化图像预测波前

复原图像

（C=0.01）

复原图像

（C=0.000 1）

复原图像

（C=0.000 01）

Table5

Specific values of the image recovery evaluation indexes for the observation target "resolution plate" under four turbulence intensities"

大气湍流D/r0退化图像

功率

谱比C

复原图像
MSEPSNR/dBMSEPSNR（dB）
5234.1424.440.015.2441.12
0.000 11.0647.87
0.000 015.0642.29
10403.2822.370.01248.7323.95
0.000 122.0438.57
0.000 0128.9236.52
15720.3213.330.01357.2822.78
0.000 142.7634.83
0.000 0148.7632.79
0.01420.1419.66
20896.479.680.000 162.6331.92
0.000 0167.4730.15
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