COMPUTATIONAL OPTICS AND IMAGING
Computational imaging includes all imaging methods that produce images as a result of computation on collected signals. Learn the tools to design new computational imaging methods to solve specific imaging problems. Provides an understanding of the physics of light propagation and measurement, and the computational tools to model it, including wave propagation, ray tracing, the radon transform, and linear algebra using matrix and integral operators and the computational tools to reconstruct an image, including linear inverse problems, neural networks, convex optimization, and filtered back-projection. Covers a variety of example computational imaging techniques and their applications including coded apertures, structured illumination, digital holography, computed tomography, imaging through scattering media, compressed sensing, and non-line-of-sight imaging.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2024 Learning Outcomes: 1. Apply ray and wave based light propagation models Audience: Graduate 2. Explain the process of image formation in conventional imaging systems using theory and computational models Audience: Graduate 3. Select and combine the different components required in an imaging system to perform light manipulation, collection, and image reconstruction Audience: Graduate 4. Apply the linear matrix and integral operators that model light propagation Audience: Graduate 5. Apply the linear inverse algorithms that allow an imaging system to reconstruct properties of the scene from collected data Audience: Graduate 6. Simulate different computational imaging systems and perform computation on simulated datasets Audience: Graduate 7. Understand the most common computational imaging techniques and be able to use and adapt them for their own applications Audience: Graduate Learning Outcomes: 1. Apply ray and wave based light propagation models Audience: Graduate 2. Explain the process of image formation in conventional imaging systems using theory and computational models Audience: Graduate 3. Select and combine the different components required in an imaging system to perform light manipulation, collection, and image reconstruction Audience: Graduate 4. Apply the linear matrix and integral operators that model light propagation Audience: Graduate 5. Apply the linear inverse algorithms that allow an imaging system to reconstruct properties of the scene from collected data Audience: Graduate 6. Simulate different computational imaging systems and perform computation on simulated datasets Audience: Graduate 7. Understand the most common computational imaging techniques and be able to use and adapt them for their own applications Audience: Graduate
Catalogue variants
- COMPUTATIONAL OPTICS AND IMAGING · 3 credits.