Computational Optical Imaging

Course manual 2025/2026

Course content

In this course, we deal with optical imaging methods that either only work through mathematical models and computer-aided post-processing, or at least can be improved or simplified with these methods. In some cases, this even allows us to circumvent the apparent physical limitations of imaging. In the lecture and the exercises, both the mathematical and physical basics will be worked out, the techniques will be described, and possible applications will be shown. The students will write small programs for image reconstruction or simulations from scratch.

The topics of the lecture include:

• Repetition of mathematical and computational fundamentals, physical basics of optics, including coherence, and speckles
• Light field cameras
• Fourier optics, digital holography, and using the phase for optical imaging
• Image deconvolution
• Optical scattering theory and optical diffraction tomography
• Optical coherence tomography
• Superresolution techniques
• Synthetic apertures and ptychography

Study materials

Literature

• J. W. Goodman, “Introduction to Fourier Optics”, Roberts and Company, 2005

• Scientific papers will be provided through canvas

Practical training material

• Exercises during class and homework assignments

Software

• Programming language and IDE of choice, ideally Python, Matlab, or Julia

Objectives

• Understand and be able to explain the basic principles of optical imaging, such as Fourier optics, and of computer-aided optical imaging techniques.
• Be able to list the advantages and disadvantages of special imaging techniques.
• Be able to list some applications for computer-aided optical imaging.
• Be able to implement common algorithms for the reconstruction of optical images and tomographies from acquired data, and be able to implement simple simulations of the physical imaging process.
• Be able to apply computational imaging methods, such as Digital Holography or Fourier Ptychography.
• Evaluate imaging modalities and resulting images based on their performance, particularly in comparison to other techniques and in the context of specific applications.

Teaching methods

• Laptop seminar
• Computer lab session/practical training
• Lecture
• Self-study

Learning activities

Attendance

Assessment

Additionally the students will also have to submit worked out computer code (assignments) to pass the course.

Assignments

There will be assignment for the students to work out. The assignment will not be graded but need to be completed.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• Dierck Hillmann