CS 4660

Foundations of Computational Imaging

Course Description

Course information provided by the 2025-2026 Catalog.

This course will provide students with a mathematical and algorithmic framework for computational imaging. Computational imaging blends hardware and software design to overcome the limits of traditional imaging systems. Computational imaging is the foundation for many scientific and medical imaging systems, including magnetic resonance imaging (MRI), computed tomography (CT), light-field imaging, and phase microscopy, and has enabled exciting breakthroughs, such as capturing the first image of a black hole. Students will learn how to formulate and solve imaging inverse problems, in which signals (typically images or volumes) are recovered from indirect or incomplete data using a variety of tools from optimization, signal processing, machine learning, and generative methods.

Prerequisites MATH 2210 or MATH 2310 or MATH 2940 or equivalent linear algebra course and CS 2110 or equivalent programming experience.

Last 4 Terms Offered (None)

Learning Outcomes

• Identify imaging forward models and inverse problems, and write them in a standard form.
• Distinguish “easy” and “hard” inverse problems and recognize when a reconstruction method is likely to succeed.
• Solve imaging inverse problems using a variety of classic and modern methods.

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