Summary of Transforming Color: a Novel Image Colorization Method, by Hamza Shafiq and Bumshik Lee
Transforming Color: A Novel Image Colorization Method
by Hamza Shafiq, Bumshik Lee
First submitted to arxiv on: 7 Oct 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 method combines a transformer architecture with generative adversarial networks (GANs) to create novel image colorization models that capture long-range dependencies and produce realistic colorizations. The color encoder uses random normal distributions to generate color features, which are then combined with grayscale image features to enhance overall representation. Compared to existing approaches, the proposed method demonstrates superior performance by leveraging the transformer’s ability to capture long-range dependencies and generate realistic colorizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to add color to black-and-white images using artificial intelligence. The method uses a special kind of neural network called a transformer to help the AI understand what makes colors look good together, and then another type of neural network called a GAN to make sure the resulting colored image looks realistic. The results show that this new method works better than other methods for colorizing images. |
Keywords
» Artificial intelligence » Encoder » Gan » Neural network » Transformer
