CS 6662 - Computational Imaging

Fall. 3 credits. Student option grading.

Prerequisite: knowledge of linear algebra and working knowledge of Python. Knowledge of convex optimization, computer vision, and machine learning are recommended but not required.

K. Monakhova.

Computational imaging is the holistic design of imaging systems together with algorithms, blending ideas from computer vision, optics, imaging, and machine learning to overcome the limits of traditional cameras and imaging systems (e.g. capturing the first image of the black hole and imaging light-fields). This course will provide an overview of the state of the art in computational imaging. We will learn how to mathematically model different aspects of imaging systems, such as noise, aberrations, and light propagation. In addition, we will learn how to formulate and solve imaging inverse problems using both classical and modern deep-learning-based approaches. Throughout the course, we will discuss exciting active research topics such as lensless imaging, compressive imaging, phase microscopy, time-of-flight imaging, and tomography. The class will culminate in an open-ended final project.

Outcome 1: Mathematically model different aspects of imaging systems, including noise, aberrations, and wavelength dependence.

Outcome 2: Formulate and solve imaging inverse problems for several imaging systems (e.g. deconvolution, denoising, tomography, phase imaging) using several different methods.

Outcome 3: Differentiate and distinguish different inverse problem algorithms, from classic to deep methods.