Summary of Visible and Infrared Image Fusion Using Encoder-decoder Network, by Ferhat Can Ataman et al.
Visible and Infrared Image Fusion Using Encoder-Decoder Network
by Ferhat Can Ataman, Gözde Bozdaği Akar
First submitted to arxiv on: 11 Dec 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 The novel learning-based solution for multispectral image fusion, presented in this paper, combines infrared and visible spectrum images to enhance perceptual quality. The proposed method uses only convolutional and pooling layers, along with a loss function that utilizes no-reference quality metrics. This approach is evaluated qualitatively and quantitatively on various datasets, demonstrating better performance compared to state-of-the-art methods. Additionally, the network’s size allows for real-time execution on embedded devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to combine images with different types of information. The goal is to create a single image that shows more detail and is easier to understand. The researchers developed a special kind of artificial intelligence (AI) model that can take two kinds of images, one in infrared light and the other in visible light, and merge them into one high-quality image. This new method works better than previous methods and can be used on devices like smartphones. |
Keywords
» Artificial intelligence » Loss function
