Programming Project #2: Image Alignment and Enhancement
Images of the Russian Empire: Colorizing the Prokudin-Gorskii photo collection
CS498: Computational Photography

Due Date: 11:59pm on Monday, Sept. 26, 2011


Sergei Mikhailovich Prokudin-Gorskii (1863-1944) [Сергей Михайлович Прокудин-Горский, to his Russian friends] was a man well ahead of his time. Convinced, as early as 1907, that color photography was the wave of the future, he won Tzar's special permission to travel across the vast Russian Empire and take color photographs of everything he saw. And he really photographed everything: people, buildings, landscapes, railroads, bridges... thousands of color pictures! His idea was simple: record three exposures of every scene onto a glass plate using a red, a green, and a blue filter. Never mind that there was no way to print color photographs until much later -- he envisioned special projectors to be installed in "multimedia" classrooms all across Russia where the children would be able to learn about their vast country. Alas, his plans never materialized: he left Russia in 1918, right after the revolution, never to return again. Luckily, his RGB glass plate negatives, capturing the last years of the Russian Empire, survived and were purchased in 1948 by the Library of Congress. The LoC has recently digitized the negatives and made them available on-line.



In this project, you will apply the concepts of template matching and coarse-to-fine search, which are key techniques for object recognition, tracking, optical flow, and a variety of other useful processes and applications. Here, your goal is to take the digitized Prokudin-Gorskii glass plate images and, using image processing techniques, automatically produce a color image with as few visual artifacts as possible. In order to do this, you will need to extract the three color channel images, place them on top of each other, and align them so that they form a single RGB color image. Some starter code is available here; do not feel compelled to use it. We will assume that a simple x,y translation model is sufficient for proper alignment. However, the full-size glass plate images are very large, so your alignment procedure will need to be relatively fast and efficient.


example negative A few of the digitized glass plate images (both hi-res and low-res versions) will be placed in the following zip file (note that the filter order from top to bottom is BGR, not RGB!): data. Your program will take a glass plate image as input and produce a single color image as output. The program should divide the image into three equal parts and align the second and the third parts (G and R) to the first (B). For each image, you will need to print the (x,y) displacement vector that was used to align the parts.

The easiest way to align the parts is to exhaustively search over a window of possible displacements (say [-15,15] pixels), 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 simplest one is just the L2 norm also known as the Sum of Squared Differences (SSD) distance which is simply sum(sum((image1-image2).^2)). Another is normalized cross-correlation (NCC), the dot product of the two vectors after normalizing them to be zero-mean, unit-deviation (see slides for Sept 2). Note that in this particular 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.

Exhaustive search will become prohibitively expensive if the pixel displacement is too large (which will be the case for high-resolution glass plate scans). In this case, you will need to implement a faster search procedure such as 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.

Your job will be to implement an algorithm that, given a 3-channel image, produces a color image as output. Implement a simple single-scale version first, using for loops, searching over a user-specified window of displacements. The above-linked skeleton Matlab code will help you get started. Next, add a coarse-to-fine pyramid speedup to handle large images.


You'll notice that many of the images look washed out. In fact, you may find that in your own photographs, the colors are not quite as vivid as you remembered, or the contrast is not quite as strong. In this part of the project, we'll look at two simple types of enhancement. Do not use the built in Matlab functions for histogram equalization.

Contrast Enhancement: The goal is to improve the contrast of the images. The poor constrast could be due to blurring in the capture process or due to the intensities not covering the full range. Choose one image (either from Russian collection or one of yours) that has poor contrast and fix the problem. Potential fixes include Laplacian filtering, gamma correction, and histogram equalization. Explain why you chose your solution.

Color Enhancement: Now, how to make the colors brighter? You'll find that if you just add some constant to all of the pixel values or multiply them by some factor, you'll make the images lighter, but the colors won't be more vivid. The trick is to work in the correct color space. Convert the images to HSV color space and divide into hue, saturation, and value channels ([h, s, v] = rgb2hsv(im) in Matlab). Then manipulate the appropriate channel(s) to make the colors (but not the intensity) brighter. Note that you want the values to map between 0 and 1, so you shouldn't just add or multiply with some constant. Show this with at least one photograph (either the Russian or yours). Show the original image, the corrected image, and histograms of hue, saturation, and value (with 255 bins each) for the before and after images.

Bells & Whistles (Extra Points)

Remove messy borders (10 pts): 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. See an example on the bulletin board. 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.

Selective color enhancement (10 pts): Figure out how to enhance some colors while suppressing others. For instance, you may want to make the reds brighter and move other colors towards gray. This can be done with just a few lines of code. Show an example on one of your photos. For a full 10 points, you would also need some way to deal with artifacts that can arise, such as when isolated pixels get brightened.

Align with FFT (20 pts): Remember that SSD can be performed with filtering operations (expand the quadratic). Thus, for large templates, you may be able to compute SSD more efficiently using the Fourier transform (discrete fft is nlogn). Write code to find the minimum alignment under SSD using the fast Fourier transform (FFT). For one large example, compare the run-time to your coarse-to-fine search runtime. Note that you should take care to provide sufficient padding (as an argument to fft2 in Matlab) and note that if you filter an image (im) with a filter (fil) using fft2/ifft2 with padding, the output will be shifted by half of the filter width.


To turn in your assignment, place your index.html file and any supporting media in your project directory:, where "netid" is your netid (e.g., dhoiem). Also, e-mail me the zipped code in "" and include a link to the project page in the text. See project instructions for details. In the e-mail, tell me how many points you think you should get for the core project and any bells and whistles.

The web page should contain:


The basic assignment is worth 100 points, as follows:

Additionally, you could earn up to 40 points for the bells and whistles explicitly mentioned above and up to 10 points for extensions that you come up with on your own (check with prof first).

Final Advice


Many thanks to Alyosha Efros for designing the alignment portion of this project, used here with permission.