Turbulent Image

Turbulent image research focuses on mitigating the distortions and blurring caused by atmospheric turbulence in images and videos, primarily impacting long-range vision systems. Current efforts concentrate on developing deep learning models, including convolutional neural networks (CNNs) and transformers, often trained with synthetically generated turbulent images, to restore image quality and improve the performance of downstream tasks like object detection and recognition. These methods often incorporate physics-based simulations to bridge the gap between synthetic training data and real-world scenarios, and are evaluated using both objective metrics and task-specific performance measures. This research is crucial for enhancing the reliability and accuracy of various applications, including autonomous driving, surveillance, and remote sensing.