Improving direct reconstructions from partial-boundary data in electrical impedance tomographyMS20

Electrical Impedance Tomography is a highly nonlinear inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images. In this work we present several techniques to improve image quality.

This presentation is part of Minisymposium “MS20 - Advances in Reconstruction Methods for Electrical Impedance Tomography (3 parts)
organized by: Melody Alsaker (Gonzaga University) , Samuli Siltanen (University of Helsinki) .

Andreas Hauptmann (University College London)
deep learning, image deblurring, image enhancement, image reconstruction, inverse problems, partial differential equation models