3 credits
Fall 2026 Lecture
This class presents a collection of mathematical and statistical methods that form the foundation of modern computational imaging research and applications. Computational imaging seeks to form images from sensor data and is widely used in applications including consumer imaging, scientific imaging, industrial inspection, and security imaging. This class provides an advanced treatment of computational imaging based on an inverse-problems framework and blends perspectives from applied math, statistics, physics, and applications. The topics covered include stochastic modeling of images, Bayesian estimation, inverse methods, optimization, convexity, majorization techniques, constrained optimization and proximal methods, plug-and-play methods for advance prior models, the EM algorithm, Bayesian networks, Markov chains, hidden Markov models, and stochastic simulation. The underlying theory is presented in the context of applications including image restoration, tomographic reconstruction, clustering, classification, and segmentation.
Learning Outcomes1Understand how to model an imaging system.
2Understand widely used methods for solving inverse problems in imaging applications.
3Implement an image reconstruction algorithm for applications such as deconvolution and denoising.
4Understand how probability can be used to model imaging systems and images.
5Understand basic approaches to optimization of quadratic and non-quadratic functions.
6Understand MAP and ML estimation methods.
Restrictions
made by Purdue students.