Coloring the Past: Using AI to Colorize Historical Images - Final Year Project
Envision what life would have been like in Victorian England, D-Day landings or even your grandparents' wedding day. My project, "Coloring the Past," aims to bring these black-and-white memories to life using AI techniques. You may ask if image colourisation already exists. So what's the big deal? Here my project focuses on historical accuracy not just pretty colours.
Here are some examples:
Project Overview and Objectives
My project uses Generative Adversarial Networks (GANs), to colorize historical black-and-white photographs. Unlike other methods, our approach ensures the colors are true to the historical period by using era-specific color palettes. This method not only enhances the visual appeal but also preserves historical authenticity.
Develop a more historically accurate image colouration model via colour palettes and new loss function that compares historical images vs normal image colourisation.
Compare our model with existing ones using quantitative and qualitative metrics.
Collect and annotate images from various periods and regions to train and test our model.
Why is Image Colorization Hard?
Turning Greyscale to color is no easy feat. Simply because we mapping a color space of 1 dimension (Only Greyscale) into 3 dimensions. (Red, Green, Blue). Without the amount of various shades and mixing we have 10M potential colours!
Technical Explanation and Methodology
My method builds on the SPColor architecture, an exemplar-based colorization approach using semantic matching.
Here's a simplified breakdown of our process:
1. Input: Start with a grayscale image.
2. Feature Extraction: Use a pre-trained CNN (like VGG-19) to understand the image's features.
3. Semantic Segmentation: Classify each pixel to identify objects (e.g., sky, tree).
4. Time Period Reference: Use a table linking historical periods to their typical colors of that era.
5. Color Scheme Generation: Combine labels, historical colors, and other data to create a color scheme.
6. Color Propagation: Apply these colours to the image.
7. Time Period Adjustment: Fine-tune colours to match the historical period accurately.
Impact and Applications
There is potential for this Machine learning model to be used in other fields. By making historical photos more realistic and engaging. Helping historians recover photos quicker. Allowing people media colorise images and video various documentary work.
If you're interested in my work feel to get in touch.