Summary of Busref: Infrared-visible Images Registration and Fusion Focus on Reconstructible Area Using One Set Of Features, by Zeyang Zhang et al.
BusReF: Infrared-Visible images registration and fusion focus on reconstructible area using one set of features
by Zeyang Zhang, Hui Li, Tianyang Xu, Xiaojun Wu, Josef Kittler
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes BusRef, a framework that tackles both image registration and fusion in a single step. Currently, image fusion algorithms rely on strictly registered input images to produce accurate results. However, real-world scenarios often involve non-aligned images. To address this issue, the authors focus on Infrared-Visible (IV) image registration and fusion. The BusRef framework consists of three stages: coarse registration, fine registration, and fusion. The proposed approach enables more robust IVRF and reduces the influence of non-reconstructible regions on loss functions. Additionally, a gradient-aware fusion network is designed to preserve complementary information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers work with mixed images from different cameras. Right now, image combining algorithms need perfectly aligned input pictures to produce good results. But in real life, camera views don’t always match up. The authors develop a new approach called BusRef that combines registration and fusion into one step. They focus on infrared and visible light images, which are used together for tasks like object detection. The BusRef framework has three stages: first, it gets a rough idea of how the images should line up, then it fine-tunes this alignment, and finally, it combines the images. This approach makes image registration and fusion more robust and reliable. |
Keywords
» Artificial intelligence » Alignment » Object detection
