Deep Learning Techniques for Atmospheric Turbulence Removal: A Review

TL;DR

This review discusses how deep learning methods, including Transformers, SWIN, and Mamba, are used to effectively mitigate atmospheric turbulence effects on imagery, improving scene interpretation and object tracking.

Contribution

It provides a comprehensive comparison of recent deep neural network architectures for atmospheric turbulence removal, highlighting their advantages over traditional methods.

Findings

Deep learning models outperform conventional approaches in turbulence mitigation.

Transformers, SWIN, and Mamba show promising results in spatio-temporal distortion correction.

Deep neural networks enable faster processing suitable for small devices.

Abstract

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.