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.
Specifically, the intended learning outcomes are:
1. The student is able to read scientific papers from the field of computational imaging and tomography and explain their contents (level: Understand)
2. The student is able to ask scientifically formulated questions about papers from the field of computational imaging and tomography (level: Understand)
3. The student is able to read algorithmic papers from the field of computational imaging and tomography and create a correct and clearly readable implementation of the described algorithm (level: Create)
4. The student is able to design a solid computational study investigating properties and performance of computational imaging and tomography algorithms from literature (level: Create)
5. The student is able to write a clear and correct research-style paper about computational imaging and tomography algorithms, using their own words and notation (level: Create)
6. The student is able to create an assembly of paper, code, and documentation that is sufficient to reproduce the computational experiments of the paper (level: Create)
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
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:
Presentation about a paper from literature (30%)
Peer feedback and class participation (20%)
Written report/paper and accompanying code implementation (50%)
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place. To pass the course, the student needs to give a presentation and submit a written paper/report with accompanying code. In case of a fail grade, there is an opportunity to redo the written report.
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
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: Prof.dr. Joost Batenburg & Dr. Daniel Pelt
Website: Brightspace
Course Website: https://dmpelt.github.io/liacs-cito
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.