Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers
Atmospheric turbulence creates complex spatial and temporal distortions that significantly reduce image quality. This work compares two deep learning methods for turbulence reduction: Generative Adversarial Networks (GANs) and Transformer-based models. Both methods are trained and tested on the SOTIS dataset, which includes multi-frame sequences under different turbulence conditions with ground truth references. Our results show an important finding: GAN-based models perform better in severely degraded, low-quality situations, while Transformer-based models achieve better results in moderate to high-quality settings, especially in correcting geometric distortions. We also enhance the Turb...
| Authors | Juan S. Carrillo, Jérôme Gilles |
|---|---|
| Primary category | eess.IV |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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