Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
It addresses the problem of image degradation for applications like object classification and tracking, but it is a review paper, so it is incremental in nature.
This paper reviews deep learning techniques for removing atmospheric turbulence from imagery, comparing the performance of state-of-the-art neural networks like Transformers, SWIN, and Mamba to mitigate spatio-temporal distortions.
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions.