Old Image Colorization
A deep learning-based approach for automatic colorization of grayscale images, enhancing visual appeal and historical restoration.
Project Overview
This project focuses on restoring and colorizing grayscale images using deep learning models. By leveraging generative adversarial networks (GANs) and transformer-based architectures, the system adds realistic colors to black-and-white images while preserving details and textures.
Features
- AI-Powered Colorization: Uses GANs and transformers to generate realistic colors.
- Historical Image Restoration: Enhances and colorizes old, degraded photos.
- Automated Pipeline: Fully automated colorization process with minimal user input.
- High-Resolution Outputs: Capable of producing high-fidelity colored images.
Challenges & Solutions
- Color Bleeding & Artifacts: Optimized model to minimize inconsistencies.
- Semantic Ambiguity: Leveraged contextual understanding using transformers.
- Real-Time Processing: Applied model acceleration techniques for faster inference.
Results
The system produces high-quality colorized images, making it useful for historical archives, entertainment, and artistic applications.
Visual representation of the transformer-based model architecture used for image colorization.