Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography

DOI10.1007/s10915-020-01295-wOpenAlexW3081340994MaRDI QIDQ2199696

Damiana Lazzaro, G. Scrivanti, Serena Morigi, Andrea Samoré, Martin Huska

Publication date: 14 September 2020

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-020-01295-w

zbMATH Keywords

variational approachelectrical impedance tomographyill-posed inverse problemsspatially-adaptive reconstruction

Mathematics Subject Classification ID

Numerical analysis (65-XX)

Related Items (4)

Mumford–Shah regularization in electrical impedance tomography with complete electrode model ⋮ Deep-plug-and-play proximal Gauss-Newton method with applications to nonlinear, ill-posed inverse problems ⋮ Enhancing electrical impedance tomography reconstruction using learned half-quadratic splitting networks with Anderson acceleration ⋮ Learning nonlinear electrical impedance tomography

Uses Software

• EIDORS

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• Nonlinear total variation based noise removal algorithms
• Convex image denoising via non-convex regularization with parameter selection
• Review on electrical impedance tomography: artificial intelligence methods and its applications
• A Backprojection Algorithm for Electrical Impedance Imaging
• A direct D-bar reconstruction algorithm for recovering a complex conductivity in 2D
• Newton regularizations for impedance tomography: a numerical study
• Existence and Uniqueness for Electrode Models for Electric Current Computed Tomography
• A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
• A convex-nonconvex variational method for the additive decomposition of functions on surfaces
• Open issues of stability for the inverse conductivity problem
• Sparsity-Inducing Nonconvex Nonseparable Regularization for Convex Image Processing

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