Deterministic versus stochastic level-set regularization in nonlinear phase contrast tomography

DOI10.1080/17415977.2016.1201662zbMath1368.92098OpenAlexW2473149294MaRDI QIDQ5348707

Bruno Sixou

Publication date: 18 August 2017

Published in: Inverse Problems in Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/17415977.2016.1201662

zbMATH Keywords

inverse problemsX-ray imagingphase contrast tomography

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Numerical methods for integral transforms (65R10)

Cites Work

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• Multiple level sets for piecewise constant surface reconstruction in highly ill-posed problems
• A Mumford-Shah level-set approach for the inversion and segmentation of X-ray tomography data
• Incorporating topological derivatives into level set methods.
• Absorption and phase retrieval with Tikhonov and joint sparsity regularizations
• Convergence rates for Kaczmarz-type regularization methods
• Stochastic motion and the level set method in computer vision: stochastic active contours
• Coherent X-Ray Optics
• Reconstruction of the complex refractive index in nonlinear phase contrast tomography
• On multiple level-set regularization methods for inverse problems
• Nonlinear regularization methods for ill-posed problems with piecewise constant or strongly varying solutions
• Diffusions for Global Optimization
• Diffusion for Global Optimization in $\mathbb{R}^n $

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