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Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications.
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S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Multi-stage progressive image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 14 821–14 831
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2022
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R. Yasarla and V. M. Patel, “CNN-Based restoration of a single face image degraded by atmospheric turbulence,” IEEE Transactions on Biometrics, Behavior, and Identity Science , vol. 4, no. 2, pp. 222–233, 2022
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S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 5728–5739
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N. Chimitt, X. Zhang, Z. Mao, and S. H. Chan, “Real-time dense field phase-to-space simulation of imaging through atmospheric turbulence,” IEEE Transactions on Computational Imaging , vol. 8, pp. 1159–1169, 2022
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S. H. Chan and N. Chimitt, “Computational imaging through atmospheric turbulence,” Foundations and Trends® in Computer Graphics and Vision , vol. 15, no. 4, pp. 253–508, 2023
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N. Anantrasirichai, “Atmospheric turbulence removal with complex-valued convolutional neural network,” Pattern Recognition Letters , vol. 171, pp. 69–75, 2023
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