Imaging with Unstructured Adaptive Meshes

Explore adaptive mesh techniques for imaging problems in this one-hour virtual seminar presented by the Society for Industrial and Applied Mathematics. Delve into the challenges of discretizing partial differential equations and integral equations for numerical solutions in imaging applications. Learn how Professor Erkki Somersalo from Case Western Reserve University addresses the trade-off between resolution and computational cost through mesh adaptation. Discover how incorporating discretization into the inverse problem can potentially improve imaging results. Gain insights into topics such as approximation error, CT attenuation, source problems, metrics, measuring principles, prior models, log post theory, IIAS algorithms, sparse representations, and analog meshing. Enhance your understanding of advanced imaging techniques and their mathematical foundations in this comprehensive talk from the 37th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series.

Introduction
Who is involved
Background
Approximation error
CT
Attenuation
Source Problem
Metric
Measuring Principle
Prior Model
Log Post Theory
IIAS Algorithm
Sparse Representations
Two Cases
Analog Meshing