A short analysis of BigColor for image colorization.
Resumen:
This work analyzes the BigColor method, a fully automatic colorization approach that aims to meet the challenge of providing realistic and vivid colorization for complex and diverse images in real-world scenarios. The method is a BigGAN-inspired encoder-generator network, using a spatial feature map, enabling single forward-pass colorization, supporting arbitrary input resolutions, and producing multimodal colorization results. We provide a short analysis of the method’s results and highlight some limitations alongside its achievements.
| 2024 | |
|
Colorization Generative color prior BigGAN |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
|
https://www.ipol.im/pub/art/2024/542/
https://hdl.handle.net/20.500.12008/43955 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| Sumario: | This work analyzes the BigColor method, a fully automatic colorization approach that aims to meet the challenge of providing realistic and vivid colorization for complex and diverse images in real-world scenarios. The method is a BigGAN-inspired encoder-generator network, using a spatial feature map, enabling single forward-pass colorization, supporting arbitrary input resolutions, and producing multimodal colorization results. We provide a short analysis of the method’s results and highlight some limitations alongside its achievements. |
|---|
