Summary of Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors, by P. Hill et al.
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors
by P. Hill, N. Anantrasirichai, A. Achim, D.R. Bull
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed self-supervised learning method addresses the challenge of atmospheric turbulence in visual imagery interpretation by leveraging accelerated Deep Image Prior (DIP) with pixel shuffling and temporal sliding window integration. This approach learns spatio-temporal priors efficiently, effectively mitigating distortions without relying on large datasets or ground truth. The method is not dataset-dependent, only requiring the single sequence being processed. Qualitative and quantitative experiments demonstrate improved visual quality results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Atmospheric turbulence in images can make it hard to understand what’s happening. To fix this, scientists came up with a new way to process images using machine learning. This method doesn’t need a lot of examples or the correct answer beforehand. Instead, it learns from the single image being processed and makes it look better. The approach is fast and works well, making distorted images clearer. |
Keywords
» Artificial intelligence » Machine learning » Self supervised
