Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2201001(2022)
Rapid Restoration of Turbulent Degraded Images Based on Bidirectional Multi-Scale Feature Fusion
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.
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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
Category: Atmospheric Optics and Oceanic Optics
Received: Sep. 9, 2021
Accepted: Sep. 27, 2021
Published Online: Sep. 19, 2022
The Author Email: Wu Xiaoqing (xqwu@aiofm.ac.cn)