Characterizing Atmospheric Turbulence and Removing Distortion in Long-range Imaging

Atmospheric turbulence distorts the path of light passing through the air. When capturing images at long range, the effects of this turbulence can cause substantial geometric distortion and blur in images and videos, degrading image quality. These become more pronounced with greater turbulence, scaling with the refractive index structure constant, Cn2. Removing effects of atmospheric turbulence in images has a range of applications from astronomical imaging to surveillance. Thus, there is great utility in transforming a turbulent image into a ``clean image" undegraded by turbulence. However, as the turbulence is space- and time-variant and statistically random, no closed-form solution exists for a function that performs this transformation. Prior attempts to approximate the solution include spatio-temporal models and lucky frames models, which require many images to provide a good approximation, and supervised neural networks, which rely on large amounts of simulated or difficult-to-acquire real training data and can struggle to generalize. The first contribution in this thesis is an unsupervised neural-network-based model to perform image restoration for atmospheric turbulence with state-of-the-art performance. The model consists of a grid deformer, which produces an estimated distortion field, and an image generator, which estimates the distortion-free image. This model is transferable across different datasets; its efficacy is demonstrated across multiple datasets and on both air and water turbulence. The second contribution is a supervised neural network to predict Cn2 directly from the warp field. This network was trained on a wide range of Cn2 values and estimates Cn2 with relatively good accuracy. When used on the warp field produced by the unsupervised model, this allows for a Cn2 estimate requiring only a few images without any prior knowledge of ground truth or information about the turbulence.