COMP 9517

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COMP 9517
T1, 2020
Assignment 1: Specification
Maximum marks achievable: 10 marks
This assignment is worth 10% of the total course marks.
Deliverables: You will submit a report (maximum 5 pages) briefly explaining the approach you
have taken in Tasks 1, 2 and 3 and include some sample input images and the intermediate and
final images obtained. You must also submit the Python source code files.
Submission: The assignment files should be submitted online via WebCMS. Instructions for
submission will be posted closer to the deadline.
Tip: You are advised to use OpenCV 3+ with python 3+. Jupyter notebook files are
preferred as source files.
Objectives:
This assignment is aimed at familiarisation with basic image processing methods. It also
introduces you to common image processing and analysis tasks using OpenCV. After
completing this assignment, you will learn how to:
1. Open and read image files
2. Perform simple mathematical operations on images
3. Construct and manipulate image pyramids
4. Carry out translation-based alignment
5. Perform image adjustment and restoration
Task:
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
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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
here for more details. At the beginning of the 20th century, Prokudin-Gorsky embarked on a
multi-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 digitized
by the US Library of Congress.
The objective of this assignment is to produce high quality colour reconstructions from
The assignment files should be submitted online.
Instructions for submission will be posted closer to the deadline.
Deadline for submission is week 4 Monday March 9th, 23:59:59
Prokudin-Gorsky‘s negatives using simple image processing techniques.
You will need to extract images for the individual colour channels, align them and form a single
colour image. For this assignment, it is sufficient to use an x, y translation-based transformbut
feel free to implement other methods. You must use OpenCV with Python to complete this
assignment.
Instructions:
Task 1 (4 marks)
Download the high-resolution negatives from Webcms3 (Course Work → Assessments →
Assignment 1 → Assignment 1 Images (20T1)). Write 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 channels and then align the second and third channels to
the first, displaying the resulting offsets for each channel.
Figure 1: High quality colour reconstruction example.
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 normalized cross correlation (NCC).
Task 2 (4 marks)
Searching through all offsets can become computationally expensive for high 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.
You should first implement an algorithm that can perform reconstruction on low resolution
images (Task 1) and only then try to modify your code to handle high resolution images (Task
2). It should be easy to reuse much of the code.
Task 3 (2 marks)
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.
Hint: For Task 3, you will need to choose images from the Library of Congress collection that
best demonstrate your enhancements.
This assignment is worth 10% of the course total. Tasks 1, 2 and 3 must be completed to
complete the assignment and will be marked against the maximum mark achievable.
Deliverables: You should submit a short report (up to 5 pages MAXIMUM) along with
your python code. This report should briefly explain the approach you have taken to align
colour channelsfor Task 1, the modifications you performed to deal with high resolution images
in Task 2, and the adjustments you made for Task 3. You may also include details of any
enhancements you have implemented. Include results for at least two (2) of the provided highresolution
negatives.
The instructions for uploading the report will be released before the submission date.
Copyright: Arcot Sowmya, CSE, UNSW, with acknowledgements to COMP 9517 teaching
team past and present.
21 February 2020

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