Acta Optica Sinica, Volume. 40, Issue 21, 2111001(2020)

Deep Learning Based Image Restoration Method of Optical Synthetic Aperture Imaging System

Ju Tang1,2,3, Kaiqiang Wang1,2,3, Wei Zhang1,2,3, Xiaoyan Wu4, Guodong Liu4, Jianglei Di1,2,3、*, and Jianlin Zhao1,2,3、**
Author Affiliations
  • 1School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
  • 2Shaanxi Key Laboratory of Optical Information Technology, Xi'an, Shaanxi 710129, China
  • 3Key Laboratory of Material Physics and Chemistry Under Extraordinary Conditions, Ministry of Education, Xi'an, Shaanxi 710129, China
  • 4Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang, Sichuan 621900, China
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    The decrease of the frequency response of the optical transfer function in the optical synthetic aperture imaging system will inevitably lead to image blur. Therefore, it is usually necessary to use the Wiener filtering or blind deconvolution algorithm to achieve image restoration, and clear and high-resolution images are obtained finally. A deep learning frame based on a U-shaped convolutional neural network is proposed. The data set is constructed by the MATLAB software to train the network. The image restoration effects of the trained U-shaped network and blind deconvolution algorithm are compared. The numerical simulation results show that the U-shaped network has strong recovery ability, generalization ability, and versatility in the image restoration based on the optical synthetic aperture imaging system under the condition of weak noise. It can realize fast blind restoration for images and has potential application prospects.

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    Ju Tang, Kaiqiang Wang, Wei Zhang, Xiaoyan Wu, Guodong Liu, Jianglei Di, Jianlin Zhao. Deep Learning Based Image Restoration Method of Optical Synthetic Aperture Imaging System[J]. Acta Optica Sinica, 2020, 40(21): 2111001

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 19, 2020

    Accepted: Jul. 15, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Di Jianglei (jiangleidi@nwpu.edu.cn), Zhao Jianlin (jlzhao@nwpu.edu.cn)

    DOI:10.3788/AOS202040.2111001

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