Summary of Multi-scale Hsv Color Feature Embedding For High-fidelity Nir-to-rgb Spectrum Translation, by Huiyu Zhai et al.
Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation
by Huiyu Zhai, Mo Chen, Xingxing Yang, Gusheng Kang
First submitted to arxiv on: 25 Apr 2024
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 The proposed Multi-scale HSV Color Feature Embedding Network (MCFNet) tackles the challenging task of translating Near-Infrared (NIR) to Red-Green-Blue (RGB) spectral domains. The MCFNet decomposes this process into three sub-tasks, including NIR texture maintenance, coarse geometry reconstruction, and RGB color prediction. It achieves this through a series of escalating resolutions, progressively enriching images with color and texture fidelity in a scale-coherent fashion. The proposed method demonstrates significant performance gains over the NIR image colorization task. This is achieved by using three key modules: the Texture Preserving Block (TPB), the HSV Color Feature Embedding Module (HSV-CFEM), and the Geometry Reconstruction Module (GRM). The MCFNet outperforms existing methods in terms of texture detail fidelity and diverse color variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to translate Near-Infrared (NIR) images into Red-Green-Blue (RGB) images. This is important because NIR images often have different colors than RGB images, but we want them to look similar. The method uses three steps: first, it keeps the texture of the NIR image looking good, then it creates a rough outline of the shape, and finally, it adds color to the image. This helps make the image look more natural and realistic. The new method is better than existing methods at keeping the details of the original image and creating different colors. |
Keywords
» Artificial intelligence » Embedding
