Summary of Equivariant Imaging For Self-supervised Hyperspectral Image Inpainting, by Shuo Li et al.
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting
by Shuo Li, Mike Davies, Mehrdad Yaghoobi
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 This paper presents a novel hyperspectral imaging (HSI) inpainting algorithm called Hyperspectral Equivariant Imaging (Hyper-EI). HSI is crucial for various applications, including earth observation, medical imaging, and astronomy. The conventional push-broom scanning approach in remote sensing often results in incomplete or corrupted observations due to platform movements or lack of accurate digital elevation maps. To address this issue, Hyper-EI is a self-supervised learning-based method that doesn’t require extensive training datasets or pre-trained models. Experimental results demonstrate state-of-the-art inpainting performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to take a picture of the Earth from space, but some parts are missing because your plane moved during the photo shoot! This is what happens in hyperspectral imaging, where we try to capture detailed pictures of objects using different wavelengths. To fix these incomplete images, scientists have developed an innovative new method called Hyper-EI. Unlike other methods that need lots of training data, Hyper-EI can work with minimal information and still produce amazing results! |
Keywords
» Artificial intelligence » Self supervised
