An adaptive lucky imaging method for turbulence‐degraded image restoration

Abstract When capturing distant targets, the video sequence images are affected by atmospheric turbulence, resulting in distortion and blur. In order to restore the degraded images due to atmospheric turbulence in video sequences, this article combines lucky imaging with generative adversarial networks for the first time. The idea of lucky imaging is employed to eliminate geometric distortions, followed by the use of generative adversarial networks to address the blur issue. Additionally, an adaptive restoration method targeting turbulence intensity is proposed to improve the computational efficiency of the proposed approach. Experimental results demonstrate that the combined restoration method of lucky imaging and generative adversarial networks outperforms classical lucky imaging. Specifically, compared to classical lucky imaging, the Brenner gradient function, Laplacian gradient function, Spatial Median Difference (SMD), Entropy, Energy gradient function, PIQE, and Brisque indicators improve by 7.7%, 13.1%, 3.6%, 4.1%, 2.1%, 26.6% and 21.54% (all evaluation indicators in the above improvement rates have undergone logarithmic transformation), respectively. Meanwhile, the proposed adaptive restoration method can improve efficiency by 28%, with greater efficiency gains observed with larger datasets.