Confidence and Misplaced Confidence in Image Reconstruction
Forming the image from a CAT scan and taking the blur out of vacation pictures are problems that are ill-posed. By definition, small changes in the data to an ill-posed problem make arbitrarily large changes in the solution. How can we hope to solve such problems using noisy data and inexact computer arithmetic?
In this talk we discuss the use of side conditions and bias constraints to improve the quality of solutions. We discuss their impact on solution algorithms and show their effect on our confidence in the results.
Dianne O’Leary, University of Maryland at College Park