Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912040359034880 |
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| author | Hill, Paul Anantrasirichai, Nantheera Achim, Alin Bull, David |
| author_facet | Hill, Paul Anantrasirichai, Nantheera Achim, Alin Bull, David |
| contents | The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Techniques for Atmospheric Turbulence Removal: A Review Hill, Paul Anantrasirichai, Nantheera Achim, Alin Bull, David Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions. |
| title | Deep Learning Techniques for Atmospheric Turbulence Removal: A Review |
| topic | Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2409.14587 |