Admission requirements
Students must be highly proficient in Python programming and writing reports and have affinity with mathematics (linear algebra) and machine learning. Students are expected to be comfortable with high-dimensional vector spaces and linear operators (basis transformations, Fourier transforms). We will work intensively on state of the art research topics in computational imaging and tomography.
Description
Computational imaging concerns the computation of images from various types of physical measurements, using computationally intensive algorithms. It typically involves aspects from physics (modelling the measurements), mathematics (modelling the inverse problem), and computer science (algorithms and high-performance computing). A famous example of computational imaging is tomography, where a 3D image of an object is formed by acquiring a series of projection images (i.e. X-ray photos) from a range of angles, and then computing the image through a series of algorithmic steps.
In this seminar course, the topics of computational imaging and tomography will be introduced in lectures, after which a series of more advanced topics will be treated by studying research papers.
The introduction part covers:
Modelling of computational imaging systems (basic physics models)
Direct inversion methods
Iterative solvers
Algorithmic aspects and computational performance
Limited data problems
Machine learning in computational imaging
Advanced topics may include:
End-to-end learning for computational imaging
Digital twin systems
Real-time tomography
Generative modelling in computational imaging
Course objectives
The first objective of the course is that the student learns about computational imaging and tomography, and how it is composed of an interplay between physics models, mathematics, and computation.
We then study some of the latest research papers in advanced topics in computational imaging and tomography. Students learn about the latest research, by reading, understanding, implementing and presenting recent scientific insights in those fields.
A paper will be chosen, the student will:
1. reimplement (part of) the work,
2. present their work,
3. write a paper about it.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of instruction
The course will start with four lectures introducing the topics of computational imaging and tomography, given by the course instructors, followed by student presentations about a research topic & implementation (with peer feedback) and papers (with feedback from lecturers). Together we study recent literature on selected topics in the fields of computational imaging and tomography.
Course load
Hours of study: 168:00 hrs (= 6 EC)
Lectures: 26 hrs
Practicals: 26 hrs
At home preparation: 116 hrs
Assessment method
The final grade is determined by:
Presentations
Active participation
A written report/paper
Peer review of a programming implementation
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
The book P.C. Hansen et al., Computed Tomography: Algorithms, Insight, and Just Enough Theory will serve as a reference.
The reading list will be selected at the time of the course, depending on the particular subjects of study. See the course page of Brightspace for more information.
Registration
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Contact
Lecturer: Prof.dr. Joost Batenburg & Dr. Daniel Pelt
Website: Brightspace
Course Website: https://dmpelt.github.io/liacs-cito
Remarks
None.