An introduction to the mathematics of compressive imaging

This is the page for the keynote presentation by Dr. Ben Adcock.

Abstract

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. The quest for ever-better image reconstruction algorithms involves a wealth of different mathematical topics, from basic linear algebra to functional and harmonic analysis, PDEs, high-dimensional probability, convex analysis and optimization, and most recently, the mathematics of deep neural networks and deep learning. In this talk I will introduce a range of different techniques for image reconstruction. I will highlight the crucial and varied role that mathematics plays in this field, and discuss both recent advances and future challenges and prospects.