Summary of Physics and Deep Learning in Computational Wave Imaging, by Youzuo Lin et al.
Physics and Deep Learning in Computational Wave Imaging
by Youzuo Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John Greenhall, Cristian Pantea, Mark A. Anastasio, Brendt Wohlberg
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 This paper reviews the application of deep learning techniques to Computational Wave Imaging (CWI), which extracts hidden structure and physical properties of a volume of material by analyzing wave signals. The authors categorize current approaches into physics-based methods, which provide high-resolution estimates but can be computationally intensive, and machine learning-based methods, which offer an alternative perspective. The review presents a structured framework consolidating existing research across domains like computational imaging, wave physics, and data science. It highlights the benefits of deep learning in addressing CWI challenges, including seismic exploration, acoustic imaging, non-destructive testing, and ultrasound computed tomography. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special computer programs called “deep learning” to help us better understand things like the inside of the Earth or medical images. It looks at different ways that people have used these programs to create detailed pictures of what’s inside a material, like rocks or bodies. The authors say that some methods are good for getting very detailed pictures but can take a long time to do so, while others use computers to help figure things out. They also talk about how this technology could be used in different fields like geology and medicine. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
