<p> 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.</p>
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<p>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. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22590076 |
| Date | 12 April 2023 |
| Creators | Zhiyuan Mao (15209827) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Image_Restoration_Methods_for_Imaging_through_Atmospheric_Turbulence/22590076 |
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