Sergei Prokudin Gorsky(1863-1944) was a Russian photographer and chemist whose collection of colour photographs is the oldest surviving to this date. He used a camera that took a sequence of three black and white exposures using blue, red and green filters. By projecting the three images using colored light it was then possible to recover the original colours. See herefor more details. At the beginning of the 20th century, Prokudin-Gorsky embarked on a many year project to systematically document the life of the Russian Empire by means of the new colour imaging technology. He then took many of the resulting negatives with him on emigration following the revolution of 1917 and they were eventually purchased and digitizedby the US Library of Congress. The objective of this task is to produce high quality colour reconstructions from Prokudin-Gorsky's negatives using simple image processing techniques.
A program that takes any one of these files as an input and produces a corresponding colour image as output. To do this you should divide the original image into three parts and then align the second and third channels to the first, displaying the resulting offsets for each channel. A simple way to perform the alignment is by searching through all possible offsets in some suitable range (e.g. 20 pixels for low resolution images) and computing for each a score measuring the quality of the match. Three suitable metrics include sum of squared differences (SSD), sum of absolute differences (SAD) and the normalizedcross correlation (NCC).
Searching through all offsets can become computationally expensive for large resolution images. To speed up the search procedure you can use a so - called image pyramid. An image pyramid is essentially the image at multiple scales, with scales varying by a factor of two. Alignment can then be done sequentially, starting with the highest level and incrementally updating your estimates as you go down the pyramid.
Try to improve the visual quality of the results of the basic algorithm. Some possibilities include colour and contrast adjustments, using a more sophisticated alignment procedure and automatically removing borders.One possible method I implemented is mean filter to improvise the obtained image in task_1 and task_2.Several other techniques availbe and can be used to enhance the quality of the image.
The entire implementation is implemented using python programming language and works for versions 2.7+ which also requires opencv and numpy libraries installed.
Download OpenCV and read guided tutorial: http://opencv.org/
The following commands allows the user to test the implementation.
python imageprocessing.py devillers.jpg
