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＜電子ブック＞
Time-dependent Problems in Imaging and Parameter Identification / edited by Barbara Kaltenbacher, Thomas Schuster, Anne Wald

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XIV, 456 p. 90 illus., 64 illus. in color : online resource
著者標目	Kaltenbacher, Barbara editor Schuster, Thomas editor Wald, Anne editor SpringerLink (Online service)
件　名	LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Computer vision LCSH:Numerical analysis FREE:Mathematical Applications in Computer Science FREE:Computer Vision FREE:Numerical Analysis
一般注記	1. Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data -- 2. Dynamic Inverse Problems for the Acoustic Wave Equation -- 3. Motion compensation strategies in tomography -- 4. Microlocal properties of dynamic Fourier integral operators -- 5. The tangential cone condition for some coefficient identification model problems in parabolic PDEs -- 6. Sequential subspace optimization for recovering stored energy functions in hyperelastic materials from time-dependent data -- 7. Joint Motion Estimation and Source Identification using Convective Regularisation with an Application to the Analysis of Laser Nanoablations -- 8. Quantitative OCT reconstructions for dispersive media -- 9. Review of Image Similarity Measures for Joint Image Reconstruction from Multiple Measurements -- 10. Holmgren-John Unique Continuation Theorem for Viscoelastic Systems -- 11. Tomographic Reconstruction for Single Conjugate Adaptive Optics -- 12. Inverse Problems of Single Molecule Localization Microscopy -- 13. Parameter identification for the Landau-Lifshitz-Gilbert equation in Magnetic Particle Imaging -- 14. An inverse source problem related to acoustic nonlinearity parameter imaging Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context. While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution HTTP:URL=https://doi.org/10.1007/978-3-030-57784-1

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