Computational Optics and Imaging
A graduate course in computational optics and imaging that covers light propagation physics, computational modeling, and image reconstruction techniques like CT and compressed sensing.
Summary
Recent recorded grades — Fall 2024: 3.71 GPA, 85.7% A/AB (n=7 letter grades).
What you’ll be able to do
Apply ray and wave based light propagation models
Explain the process of image formation in conventional imaging systems using theory and computational models
Select and combine the different components required in an imaging system to perform light manipulation, collection, and image reconstruction
Apply the linear matrix and integral operators that model light propagation
Apply the linear inverse algorithms that allow an imaging system to reconstruct properties of the scene from collected data
Simulate different computational imaging systems and perform computation on simulated datasets
Understand the most common computational imaging techniques and be able to use and adapt them for their own applications
Source: UW Guide 1· UW Guide 2· UW Guide 3· UW Guide 4
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Recent recorded grades — Fall 2024: 3.71 GPA, 85.7% A/AB (n=7 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Physics of light propagation and measurement
- Wave propagation, ray tracing, and the Radon transform
- Image reconstruction via linear inverse problems, neural networks, and convex optimization
- Applications including coded apertures, digital holography, CT, and compressed sensing
Skills
- Designing new computational imaging methods
- Modeling light propagation and measurement physics
- Reconstructing images using linear inverse problems, neural networks, and convex optimization
Grades
Latest available · Fall 2024— not enough history to project Spring 2027.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "297645b2e31aeb71e62d80ed05d68488b9ae0e0082eb4b4b36eed2346c893321",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}