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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 SOTI
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