Summary of Noise2noise Denoising Of Crism Hyperspectral Data, by Robert Platt et al.
Noise2Noise Denoising of CRISM Hyperspectral Data
by Robert Platt, Rossella Arcucci, Cédric M. John
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Noise2Noise4Mars (N2N4M) model is a self-supervised, data-driven architecture that removes noise from Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) images. This is crucial as sensor degradation has rendered a significant portion of the recently acquired data unusable. The N2N4M model outperforms benchmark methods on most metrics, allowing for detailed analysis of critical sites on the Martian surface, including proposed lander sites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Noise2Noise4Mars (N2N4M) model helps fix noisy Mars images. Right now, some pictures taken by a special camera called CRISM are too noisy to be useful. This makes it hard for scientists to study certain areas on the Martian surface. The N2N4M model can clean up these noisy pictures without needing any perfect examples of what the noise should look like. It does this really well and is especially helpful in space exploration where good data is hard to find. |
Keywords
* Artificial intelligence * Self supervised
