Recolorization Project 🌸🎨

This project addresses the image recolorization problem: adding colors to grayscale images when only limited information (mean intensity of R, G, and B channels) is available.
The solution is based on a U-Net architecture with residual blocks, enhanced by AdaIN conditioning inspired by StyleGAN.

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📝 Project Overview

The main idea is to leverage the separation of luminance and chrominance in the Lab color space:

• The L channel (lightness) preserves geometric and intensity information from the grayscale image.
• The ab channels (chrominance) are predicted by the network, responsible for reconstructing realistic colors.

This design reduces the complexity of the learning task, letting the model focus only on colorization.

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📂 Dataset

We use the Oxford Flowers 102 dataset, which contains 102 flower categories with varying shapes and appearances.
To simplify training:

• Images are resized to a lower resolution (128×128).
• The dataset is split into grayscale (L) and color components (ab).
• Data augmentation (geometric transformations and noise) increases robustness and reduces overfitting, since the dataset is relatively small.

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⚙️ Methodology

Data Processing

• Generator returns a tuple:
  • Input: (grayscale image, condition vector)
  • Target: ab color channels
• Training is done in Lab space.
• For inference: concatenate L + predicted ab → convert back to RGB.

Baseline

As a reference, a naive baseline uniformly assigns colors from the provided palette. This model serves as a benchmark to evaluate the network’s performance.

Network Architecture

• U-Net with residual blocks ensures effective feature extraction.
• AdaIN conditioning integrates style information, guiding color prediction.
• Loss objective focuses only on chrominance channels.

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🚀 How to Run

1. Clone this repository:

   git clone https://github.com/FiloSamo/Colorization_OxfordFlowers102.git
   cd Colorization_OxfordFlowers102
   pip install -r requirements.txt
   jupyter notebook recolorization_project.ipynb

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📚 References

• Richard Zhang, Phillip Isola, and Alexei A. Efros.
  Colorful Image Colorization.
  In European Conference on Computer Vision (ECCV), 2016.
  [Paper]

• Patricia Vitoria, Lara Raad, and Coloma Ballester.
  ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution.
  In IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2445–2454, 2020.
  [Paper]

• Andrea Asperti.
  Deep Learning – Lecture Slides.
  University of Bologna, Academic Year 2024/2025.

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🧑‍💻 Author

Filippo Samorì Project developed as part of academic work in deep learning.

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📊 Results

• The network produces colorized images that are visually closer to the ground truth compared to the baseline.
• Results are evaluated qualitatively by comparing reconstructed images with real colored ones.
• Flowers with distinctive shapes and strong chromatic cues are recolored more faithfully.