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.
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
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.
The most recent timetable can be found at the Computer Science (MSc) student website.
Detailed table of contents can be found in Brightspace.
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.
Hours of study: 168:00 hrs (= 6 EC)
Lectures: 26 hrs
Practicals: 26 hrs
At home preparation: 116 hrs
The final grade is determined by:
• 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.
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.
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Lecturer: Prof.dr. Joost Batenburg & Dr. Daniel Pelt
Course Website: https://dmpelt.github.io/liacs-cito