RGB Channel Alignment

This project reconstructs full-color RGB images from grayscale photographs where the Red, Green, and Blue channels are stacked vertically.
The implementation uses ORB (Oriented FAST and Rotated BRIEF) feature matching and homography transformation in OpenCV to align channels and correct misalignments, producing high-quality reconstructed images.

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🎯 Objectives

• Split grayscale input (with 3 stacked channels) into individual B, G, and R channels.
• Use feature-based alignment (ORB + Homography with RANSAC) to correct channel misalignments.
• Warp and reconstruct aligned channels into a single RGB image.
• Save and visualize the reconstructed image.

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🛠️ Implementation Details

1. Splitting Channels

Each raw input consists of three stacked grayscale images:

• Top third → Blue channel
• Middle third → Green channel
• Bottom third → Red channel

1. Splitting Channels

Each raw input consists of three stacked grayscale images:

• Top third → Blue channel
• Middle third → Green channel
• Bottom third → Red channel

2. Channel Alignment

• ORB (Oriented FAST and Rotated BRIEF) is used to detect and describe keypoints in each channel.
• BFMatcher with Hamming distance finds correspondences between feature descriptors.
• A homography is estimated with RANSAC to robustly align channels.

3. Reconstruction

• Red and Blue are aligned relative to Green (chosen as reference).
• Channels are stacked to form an RGB image.

4. Command-Line Interface

• The program accepts --input (raw stacked image) and --output (path to save aligned result).
• It now automatically handles directories or missing extensions.

5. Final Script Usage

Run the script as: python3 untitled.py
--input /tests/test_image_1.jpg
--output /results/test_1_aligned.jpg

✅ Elample Result

Input - stacked grayscale plates

Output - aligned RGB image

⚙️ Dependencies

• Python 3.x
• OpenCV ≥ 4.5.4
• NumPy
• Matplotlib

Install with:

pip install opencv-python numpy matplotlib

🔑 Key Takeaways

• This project demonstrates how early color photography relied on careful channel alignment.
• ORB + Homography with RANSAC gives robust alignment even with perspective distortions.
• Results show good alignment, though extreme motion blur or damaged plates may need advanced techniques (e.g., pyramid search, cross-correlation).

📌 Future Improvements

• Implement pyramid alignment (multi-scale search).
• Add automatic cropping to remove misaligned borders.
• Explore mutual information alignment for robustness.

📖 References

• S. Prokudin-Gorskii Photo Collection, Library of Congress
• OpenCV ORB and Homography Documentation