• Laser & Optoelectronics Progress
  • Vol. 59, Issue 22, 2201001 (2022)
Yiming Guo1、2、3, Xiaoqing Wu1、3、*, Changdong Su1、2、3, Shitai Zhang1、2、3, Cuicui Bi1、2、3, and Zhiwei Tao1、2
Author Affiliations
  • 1Key Laboratory of Atmospheric Optics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui, China
  • 2University of Science and Technology of China, Hefei 230026, Anhui, China
  • 3Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Anhui, China
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    DOI: 10.3788/LOP202259.2201001 Cite this Article
    Yiming Guo, Xiaoqing Wu, Changdong Su, Shitai Zhang, Cuicui Bi, Zhiwei Tao. Rapid Restoration of Turbulent Degraded Images Based on Bidirectional Multi-Scale Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2201001 Copy Citation Text show less

    Abstract

    This study proposes a generative adversarial network (GAN) based on bidirectional multi-scale feature fusion to reconstruct target celestial images captured by various ground-based telescopes, which are influenced by atmospheric turbulence. This approach first constructs a dataset for network training by convolving a long-exposure atmospheric turbulence degradation model with clear images and then validates the network's performance on a simulated turbulence image dataset. Furthermore, images of the International Space Station collected by the Munin ground-based telescope (Cassegrain-type telescope) that were influenced by atmospheric turbulence are included in this study. These images were sent to the proposed neural network model for testing. Different image restoration assessment shows that the proposed network has a good real-time performance and can produce restoration results within 0.5 s, which is more than 10 times faster than standard nonneural network restoration approaches; the peak signal to noise ratio (PSNR) is improved by 2 dB?3 dB, and structural similarity (SSIM) is enhanced by 9.3%. Simultaneously, the proposed network has a pretty good restoration impact on degraded images that are influenced by real turbulence.