Image Restoration Methods for Imaging through Atmospheric Turbulence
The performance of long-range imaging systems often suffers due to the presence of atmospheric turbulence. One way to alleviate the degradation caused by atmospheric turbulence is to apply post-processing mitigation algorithms, where a high-quality frame is reconstructed from a single degraded image or a sequence of degraded frames. The image processing algorithms for atmospheric turbulence mitigation have been studied for decades, yet some critical problems remain open.
This dissertation addresses the problem of image reconstruction through atmospheric turbulence from three unique perspectives: 1) Reconstruction with the presence of moving objects using an improved classical image processing pipeline. 2) A fast simulation scheme for efficiently generating large-scale turbulence-degraded datasets for training deep neural networks. 3) A deep learning-based single-frame reconstruction method using Vision Transformer.
NSF grant 1763896
Air Force Research Lab (public release approval number 88ABW 2020-0292)
The Intelligence Advanced Research Projects Activity (IARPA) under Contract No. 2022-2110210000. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
- Doctor of Philosophy
- Electrical and Computer Engineering
- West Lafayette