Atmospheric Turbulence Removal in Long-Range Imaging Using a Data-Driven-Based Approach

Atmospheric turbulence is one of the causes of quality degradation in long-range imaging and its removal from degraded frame sequences is considered an ill-posed problem. There have been numerous attempts to address this problem. However, there is still room for improving the quality of the restored images. In contrast to the previous approaches to address this problem, in this paper, we propose a data-driven approach. First, an end-to-end deep convolutional autoencoder is trained using natural images and its encoder part is exploited to extract high-level features from all the frames in a sequence that are distorted by atmospheric turbulence. Then, the binary search algorithm and the unsupervised K-means clustering method are jointly exploited to analyze these high-level features to find the best set of frames that can help accurately reconstruct the original high-quality image. After removing the geometric distortion from the selected frames, the saliency map of the average set of the selected frames is calculated and used with the original selected frames to train an end-to-end multi-scale saliency-guided deep convolutional autoencoder network to fuse the registered frames. This network uses different scales of the input frames and their saliency maps for better fusion performance. Specifically, the fusion network learns how to fuse these sets of frames and also exploit information from their saliency map to generate an image with more details of the scene. Finally, this fused image is post-processed to boost the visual quality of the output fused image. The experimental results on both synthetically and naturally distorted sequences show the superiority of our method compared to the state-of-the-art methods.