Fall 2022 CS543/ECE549

Assignment 1: Colorizing Prokudin-Gorskii images of the Russian Empire

Due date: Monday, September 12, 11:59:59 PM

Background

Sergei Mikhailovich Prokudin-Gorskii (1863-1944) was a photographer who, between the years 1909-1915, traveled the Russian empire and took thousands of photos of everything he saw. He used an early color technology that involved recording three exposures of every scene onto a glass plate using a red, green, and blue filter. Back then, there was no way to print such photos, and they had to be displayed using a special projector. Prokudin-Gorskii left Russia in 1918. His glass plate negatives survived and were purchased by the Library of Congress in 1948. Today, a digitized version of the Prokudin-Gorskii collection is available online.

Overview

Data

Detailed instructions

Basic alignment. The easiest way to align the parts is to exhaustively search over a window of possible displacements (say [-15,15] pixels independently for the x and y axis), score each one using some image matching metric, and take the displacement with the best score. There is a number of possible metrics that one could use to score how well the images match. The most basic one is the L2 norm of the pixel differences of the two channels, also known as the sum of squared differences (SSD), which in Python is simply sum((image1-image2)**2) for images loaded as NumPy arrays. Note that in our case, the images to be matched do not actually have the same brightness values (they are different color channels), so a cleverer metric might work better. One such possibility is normalized cross-correlation (NCC), which is simply the dot product between the two images normalized to have zero mean and unit norm. Test your basic alignment solution on the first set of six lower resolution images.

Multiscale alignment. For the high-resolution glass plate scans provided above, exhaustive search over all possible displacements will become prohibitively expensive. To deal with this case, implement a faster search procedure using an image pyramid. An image pyramid represents the image at multiple scales (usually scaled by a factor of 2) and the processing is done sequentially starting from the coarsest scale (smallest image) and going down the pyramid, updating your estimate as you go. It is very easy to implement by adding recursive calls to your original single-scale implementation.

For Bonus Points

Implement and test any additional ideas you may have for improving the quality of the colorized images. For example, the borders of the photograph will have strange colors since the three channels won't exactly align. See if you can devise an automatic way of cropping the border to get rid of the bad stuff. One possible idea is that the information in the good parts of the image generally agrees across the color channels, whereas at borders it does not.

If you have other ideas for further speeding up alignment of high-resolution images, you may also implement and test those.

What to turn in

• A brief description of your implemented solution, focusing especially on the more "non-trivial" or interesting parts of the solution. What implementation choices did you make, and how did they affect the quality of the result and the speed of computation? What are some artifacts and/or limitations of your implementation, and what are possible reasons for them?
  
  
• For the multiscale solution, report on its improvement in terms of running time (feel free to use an estimate if the single-scale solution takes too long to run). For timing, you can use the python time module. For example:
  
  import time
  start_time = time.time()
  # your code
  end_time = time.time()
  total_time = end_time - start_time
  
  
• The output color image for every single input glass plate and the displacement vectors that were used to align the channels. Include outputs and displacements for both the six lower resolution images processed with your basic solution and the three high-resolution images aligned with the multiscale solution. When inserting results images into your report, you should resize/compress them appropriately to keep the file size manageable (under 20MB ideally) -- but make sure that the correctness and quality of your output can be clearly and easily judged. You will not receive credit for any results you have obtained, but failed to include directly in the report PDF file.
  
  
• Any bonus improvements you attempted, with output. Any parts of the report you are submitting for extra credit should be clearly marked as such.

Submission Instructions

To submit this assignment, you must upload the following files on Canvas:

1. Your code. The filename should be lastname_firstname_a1.py (or another Python extension).
2. A brief report in a single PDF file with all your results and discussion following this template. The filename should be lastname_firstname_a1.pdf.
3. All your output images in a single zip file. The filename should be lastname_firstname_a1.zip. Note that this zip file is for backup documentation only, in case we cannot see the images in your PDF report clearly enough. As stated above, you will not receive credit for any output images that are part of the zip file but are not shown (in some form) in the report PDF.

Please refer to course policies on academic honesty, collaboration, late days, etc.