Submitted

• Translation invariant diagonal frame decomposition of inverse problems and their regularization
  S. Göppel, J.Frickel, M. Haltmeier
  Submitted, 2022 [PDF[arxiv.org]
  
  
• Convergence analysis of critical point regularization with non-convex regularizers
  D. Obmann, M. Haltmeier
  Submitted, 2022 [PDF[arxiv.org]
  
  
• Unsupervised Joint Image Transfer and Uncertainty Quantifcation using Patch Invariant Networks
  C. Angermann, M. Haltmeier, A. R. Siyal
  Submitted, 2022 [PDF[arxiv.org] 
  



• A Joint Variational Multichannel Multiphase Segmentation Framework
  N. Gruber, J. Schwab, S. Court, E. Gizewski, M. Haltmeier
  Submitted, 2022 [arxiv.org]
  
  
• Machine Learning for Nondestructive Waer Assessment in Large Internal Combustion Engines
  C. Angermann, S. Jonsson, M. Haltmeier, A. Moravova, C. Laubichler, C. Kiesling, M. Kober, W. Fimml
  Submitted, 2021[pdf][arxiv.org]
  
  
• Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition
  A. Ebner, J. Frikel, D. Lorenz, J. Schwab, M. Haltmeier
  Submitted, 2020  [arxiv.org]
  
  
• Regularization of inverse problems by neural networks
  M. Haltmeier, L.V. Nguyen
  Submitted, 2020 [pdf ][arxiv.org]
  
  
• Unsupervised adaptive neural network regularization for accelerated radial cine MRI
  A. Kofler, M. Dewey, T. Schaeffter, C. Kolbitsch, M. Haltmeier
  Submitted, 2020 [pdf ][arxiv.org]
  
  
• Real-time photoacoustic projection imaging using deep learning
  J. Schwab, S. Antholzer, R. Nuster and M. Haltmeier
  Submitted, 2018 [pdf ] [video-1] [video-2] [video-3] [video-4] [arxiv.org]
  
  

Published 2023

• Convergence rates for the joint solution of inverse problems with compressed sensing data
  A.Ebner, M.Haltmeier
  Submitted 2022, [pdf] [arxiv.org]
  
  

Published 2022

• Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
  A. Kofler, C. Wald, T. Schaeffter, M. Haltmeier, C. Kolbitsch
  Submitted, 2022 [PDF[arxiv.org]
  
  
• Analysis for Full Field Photoacoustic Tomography with Variable Sound Speed
  L. V. Nguyen, M. Haltmeier, R. Kowar, N. Do
  SIAM J. IMAGINGSCIENCES, pp. 1213--1228, 2022 [arxiv.org]
  
  
• Data-consistent neural networks for solving nonlinear inverse problems
  Y. E. Boink, M. Haltmeier, S. Holman, J. Schwab
  Cornell University, 2022 [pdf ][arxiv.org]
  
  
• Photoacoustic inversion formulas using mixed data on finite time intervals
  F. Dreier, M. Haltmeier
  Inverse Problems, 2022, [pdf] [arxiv.org]
  
  
• Heat diffusion blurs photothermal images with increasing depth
  
  P. Burgholzer, G. Mayr, C. Thummerer, M. Haltmeier
  Journal of Applied Physics, 2022 [AIP]
  
  
• A Variational View on Statistical Multiscale Estimation
  M. Haltmeier, H. Li, A. Munk
  Annual Reviews, pp 343-372, 2021, [pdf] [arxiv.org]
  
  
• Feature Reconstruction from Incomplete Tomographic Data without Detour
  
  S. Göppel, J. Frickel, M. Haltmeier
  Journals Mathematics, 2022, [pdf] [arxiv.org]
  
  
• Convolutional Analysis Operator Learning by End-to-End Training of Iterative Neural Networks
  A. Kofler, C. Wald, T. Schaefter, M. Haltmeier, C. Kolbitsch
  IEEE Xplore, 2022, [pdf] [arxiv.org]

Published 2021

• Discretization of learned NETT regularization for solving inverse problems
  S.Antholzer, M.Haltmeier
  Journal of Imaging, 2021, [pdf] [arxiv.org]
  
  
• Augmented NETT regularization of inverse problems
  D. Obmann, L. Nguyen, J. Schwab, M. Haltmeier, 2021
  Journal of Physics Communications,  2021
  
  
• Quantitative cylinder liner wear assessment in large internal combustion engines using hand-
  held optical measurement devices and deep learning
  C. Laubichler, C. Kiesling, M. Kober, A. Wimmer, C. Angermann, M. Haltmeier, S. Jonsson
  Verlag der Technischen Universität Graz, (14 pages), 2021
  
  
• Relaxed data-consistency for limited bandwidth photoacoustic tomography
  D. Obmann, M. Haltmeier
  European Conferences on Biomedical Optics 2021 (ECBO), 2021
  
  
• Sampling and resolution in sparse view photoacoustic tomography
  M. Haltmeier, D. Obmann, F. Dreier, S. Antholzer, K. Felbermayer, F. Hinterleitner, P. Burgholzer
  European Conferences on Biomedical Optics 2021 (ECBO), 2021
  
  
• Deep structure learning using feature extraction in trained projection space.
  A.Christoph, M. Haltmeier
  
Computers & Electrical Engineering 92, No. 107097, 2021

• Recovering the Initial Data of the Wave Equation from Neumann Traces.
  F. Dreier, M. Haltmeier
  
  SIAM J. Math. Anal., 53(2), 2427–2451. (25 pages), 2021
  
  
• An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2D cine MR image reconstruction.
  Kofler, Andreas; Haltmeier, Markus; Schaeffter, Tobias; Kolbitsch, Christoph
  Medical Physics 48/5, S. 2412 - 2425, 2021
  
  
• Deep synthesis network for regularizing inverse problems.
  Obmann, Daniel; Schwab, Johannes; Haltmeier, Markus
  INVERSE PROBLEMS 37, No. 015005. ,2021
  
  
• Multiscale Factorization of the Wave Equation with Application to Compressed Sensing Photoacoustic Tomography.
  Zangerl, Gerhard; Haltmeier, Markus
  SIAM Journal on Imaging Sciences 14/2, S. 558 - 579. ,2021
  
  
• Photoacoustic reconstruction from photothermal measurements.
  Burgholzer, Peter; Thummerer, Gregor; Mayr, Günther; Haltmeier, Markus
  Oraevsky, Alexander; Wang, Lihong: Photons Plus Ultrasound: Imaging and Sensing ,2021
  
  
• Combining Reconstruction and Edge Detection in Computed Tomography.
  Frikel, Jürgen; Göppel, Simon; Haltmeier, Markus
  Palm, C.; Deserno, Th.; Handels, H.; Maier, A.; Maier-Hein, K.; Tolxdorff, Th.: Bildverarbeitung für die Medizin ,2021

Published 2020

• Sparse synthesis regularization with deep neural networks
  D. Obmann, J. Schwab, M. Haltmeier
  IEEE SampTA (9030953), pp.1-5, 2020 [pdf ] [arxiv.org]
  

• Projection-Based 2.5D U-net architecture for fast volumetric segmentation
  C. Angermann, M. Haltmeier, R. Steiger, S. Pereverzyev, E. Gizewski
  IEEE SampTA (9030861), pp.1-5, 2020 [pdf ] [arxiv.org]

• A joint deep learning approach for automated liver and tumor segmentation
  N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier
  IEEE SampTA (9030909), pp.1-5, 2020
  

• Superiorized regularization of inverse problems
  A. Gibali, M. Haltmeier
  J Appl Numer Optim 2(1), pp.63-70, 2020
  

• Breaking the resolution limit in photoacoustic imaging using positivity and sparsity
  P. Burgholzer, J. Bauer-Marschallinger, M. Hettich, M. Haltmeier
  Proc. SPIE, Vol. 11240, pp.232-238, 2020

• Photoacoustic reconstruction from photothermal measurements including prior information
  G. Thummerer, G. Mayr, M. Haltmeier, P. Burgholzer
  Photoacoustics 19, 2020

• Phospholipid acyl chain diversity controls the tissue-specific assembly of mitochondrial cardiolipins
  G. Oemer, J. Koch, Y. Wohlfarter, M.T. Alam, K. Lackner, S. Sailer, L. Neumann, H.H. Lindner, K. Watschinger, M. Haltmeier, E.R. Werner, J. Zschocke, M.A. Keller
  Cell Rep 30(12), pp.4281-4291, 2020

• Explicit inversion formulas for the two-dimensional wave equation from Neumann traces
  F. Dreier, M. Haltmeier
  SIAM J Imaging Sci 13(2), p.589–608, 2020 [pdf ] [arxiv.org]

• A machine learning framework for customer purchase prediction in the non-contractual setting
  A. Martínez, C. Schmuck, S. Pereverzyev Jr., C. Pirker, M. Haltmeier
  Eur J Oper Res 281(3), pp.588-596, 2020 [pdf ]

• NETT: solving inverse problems with deep neural networks
  H. Li, J. Schwab, S. Antholzer, M. Haltmeier
  Invers Probl, 2020 [pdf ] [arxiv.org]

• Big in Japan: Regularizing networks for solving inverse problems
  J. Schwab, S. Antholzer, M. Haltmeier
  J Math Imaging Vis 62, pp.445-455,  2020 [pdf ] [arxiv.org]
  
  
• Linking Information theory and thermodynamics in spatial resolution in photothermal and photoaccoustic imaging.
  P. Burgholzer, G. Mayr, G. Thummerer, M.Haltmeier, 
  Journal Of Applied Physics 128/17, No. 171102.
  
  
• Neural networks-based regularization for large-scale medical image reconstruction.
  A. Kofler,  M. Haltmeier, T. Schaeffter, M. Kachelrieß, M. Dewey, C. Wald, C. Kolbitsch
  Physics in Medicine & Biology 65/13, No. 135003.
  
  
• The conical Radon transform with vertices on triple line segments.
  S. Moon, M. Haltmeier
  INVERSE PROBLEMS 36/11, No. 115005
  
  
• Breaking the resolution limit in photoacoustic imaging using positivity and sparsity.
  M. Haltmeier, P. Bugholzer, J. Bauer-Marschallinger, M. Hettich,
  Proceedings of the International Society for Optical Engineering (SPIE) 11240
  
  
• Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation.
  C. Angermann, M. Haltmeier, R. Steiger, S. Pereverzyev, E. Gizewski,
  2019 13th International conference on Sampling Theory and Applications (SampTA). 8-12 July 2019, Bordeaux, France. IEEE Conference eXpress Publishing,
  
  
• Sparse Regularization of Inverse Problems by Operator-Adapted Frame Thresholding.
  J. Friekel, M. Haltmeier,
  Mathematics of Wave Phenomena. Conference on Mathematics of Wave Phenomena. Proceedings. Karlsruhe,July 23–27
  
  
• A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation.
  N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier,
  2019 13th International conference on Sampling Theory and Applications (SampTA). 8-12 July 2019, Bordeaux, France. IEEE Conference eXpress Publishing,
  
  
• Sparse Anett For Solving Inverse Problems With Deep Learning.
  D. Obmann, L. Nguyen, J. Schwab, M. Haltmeier,
  2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). April 3-7, 2020, virtual. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE),
  
• Sparse synthesis regularization with deep neural networks.
  D. Obmann, J. Schwab, M. Haltmeier,
  2019 13th International conference on Sampling Theory and Applications (SampTA). 8-12 July 2019, Bordeaux, France. IEEE Conference eXpress Publishing,

Published 2019

• Analysis of the block coordinate descent method for linear Ill-posed problems
  S. Rabanser, L. Neumann, M. Haltmeier
  SIAM J Imaging Sci 12(4), pp.1808-1832,  2019 [pdf ] [arxiv.org]

• Random 2.5D U-net for Fully 3D Segmentation
  C. Angermann, M. Haltmeier
  MLMECH, CVII-STENT, Computer Science Vol. 11794, Springer, Cham, 2019

• Full field inversion in photoacoustic tomography with variable sound speed
  G. Zangerl. M. Haltmeier, L.V. Nguyen,R.Nuster
  Appl Sci 9(8):1563, 2019 [arxiv.org]

• Reconstruction algorithms for photoacoustic tomography in heterogeneous damping media
  M. Haltmeier, L.V. Nguyen
  J Math Imaging Vis 61(7), pp.1007-1021, 2019 [pdf ] [arxiv.org]

• Photoacoustic tomography with direction dependent data: an exact series reconstruction approach
  G. Zangerl, S. Moon, M. Haltmeier
  Inverse Probl 35(11), 2019 [pdf ] [arxiv.org]
  
  
• Douglas-Rachford algorithm for magnetorelaxometry imaging using random and deterministic activations
  M. Haltmeier, G. Zangerl, P. Schier, D. Baumgarten
  Int J Appl Electrom 60(1), pp.63-78, 2019 [arxiv.org]
  
  
• Deep learning for photoacoustic tomography from sparse data
  S. Antholzer, M. Haltmeier, J. Schwab
  Inverse Probl Sci En 27(7), pp.987-1005, 2019 [pdf ] [arxiv.org]

• Operator learning approach for the limited view problem in photoacoustic tomography
  F. Dreier, S. Pereverzyev Jr., M. Haltmeier
  Comput Meth Appl Math 19(4), pp.749-764, 2019 [pdf] [arxiv.org]

• Photoacoustic image reconstruction from full field data in heterogeneous media
  M. Haltmeier, G. Zangerl, L.V. Nguyen, R. Nuster
  Proc. SPIE, Vol. 10878, 2019

• Deep Learning of truncated singular values for limited view photoacoustic tomography
  J. Schwab, S. Antholzer, R. Nuster, G. Paltauf, M. Haltmeier
  Porc. SPIE, Vol. 10878, 2019

• Learned backprojection for sparse and limited view photoacoustic tomography
  J. Schwab, S. Antholzer, M. Haltmeier
  Proc. SPIE, Vol. 10878, 2019

• NETT regularization for compressed sensing photoacoustic tomography
  A. Antholzer, J. Schwab, J. Bauer-Marschallinger, P. Burgholzer, M. Haltmeier
  Proc. SPIE, Vol. 10878, 2019

• Online and stable learning of analysis operators
  M. Sandbichler, K. Schnass
  IEEE Trans Signal Process 67(01), pp.41-53, 2019 [pdf ] [arxiv.org]

• Compressive time-of-flight 3D imaging using block-structured sensing matrices
  S. Antholzer, C. Wolf, M. Sandbichler, M. Dielacher and M. Haltmeier
  Inverse Probl 35(04), pp.18, 2019 [pdf ] [arxiv.org]

• Deep null space learning for inverse problems: convergence analysis and rates
  J. Schwab, S. Antholzer, M. Haltmeier
  Inverse Probl 35(02), pp.13, 2019 [pdf ] [arxiv.org]

• Image based fashion product recommendation with deep learning
  H. Tuinhof, C. Pirker, M. Haltmeier
  Machine Learning, Optimization, and Data Science, 4th International Conference, Volterra, Italy, 2018, Revised Selected Papers 2019 [pdf ] [arxiv.org]

Published 2018

• Variational regularization of the weighted conical Radon transform
  M. Haltmeier, D. Schiefeneder
  Inverse Probl 34(12), pp.17, 2018 [pdf ] [arxiv.org]

• Convergence radius and sample complexity of ITKM algorithms for dictionary learning
  K. Schnass
  Appl Comput Harmon A 45(1), pp.22-58, 2018 [pdf ]

• Pipe failure modelling for water distribution networks using boosted decision trees
  D. Winkler, M. Haltmeier, M. Kleindorfer, W. Rauch, F. Tscheikner-Gratl
  Struct Infrastruct E 14(10), pp.1402-1411, 2018

• A new sparsification and reconstruction strategy for compressed sensing photoacoustic tomography
  M. Haltmeier, M. Sandbichler, T. Berer, J. Bauer-Marschallinger, P. Burgholzer, L. Nguyen
  J. Acoust Soc Am 143(6), 3838, 2018 [pdf ] [arxiv.org]

• A U-Nets cascade for sparse view computed tomography
  A. Kofler, M. Haltmeier, C. Kolbitsch, M. Kachelrieß, M. Dewey
  Proc. MLMIR 2018, Lecture Notes in Computer Science, Vol. 11074, pp.91-99, Springer, Cham

• Total variation minimization compressed sensing
  F. Krahmer, C. Kruschel, M. Sandbichler
  Applied Numerical Harmonic Analysis, pp.333-358, 2018

• Photoacoustic image reconstruction via deep learning
  S. Antholzer, M. Haltmeier, R. Nuster, J. Schwab
  Proc. SPIE 10494:104944U, 2018 [pdf ]

• Stochastic proximal gradient algorithms for multi-source quantitative photoacoustic tomography
  S. Rabanser, L. Neumann, M. Haltmeier
  Entropy, 20(2):121, 2018 [pdf ] [arxiv.org]

• The averaged Kaczmarz iteration for solving inverse problems
  H. Li, M. Haltmeier
  SIAM J Imaging Sci 11(1), pp.618–642, 2018 [pdf ] [arxiv.org]

• Efficient regularization with wavelet sparsity constraints in PAT
  J. Frikel, M. Haltmeier
  Inverse Probl 34(2):024006, pp.28, 2018 [pdf ] [arxiv.org]

• Analysis of the linearized problem of quantitative photoacoustic tomography
  M. Haltmeier, L. Neumann, L. V. Nguyen, S. Rabanser
  SIAM J Appl Math 78(1), pp.457–478, 2018. [pdf ] [arxiv.org]

• Galaxy and mass assembly: automatic morphological classification of galaxies using statistical learning
  S. Sreejith, S. Pereverzyev Jr., L.S. Kelvin, F. Marleau, M. Haltmeier, J. Ebner, et al.
  Mon. Notices Royal Astron Soc 474(4), pp.5232-5258, 2018

• A Galerkin least squares approach for photoacoustic tomography
  J. Schwab, S. Pereverzyev Jr., M. Haltmeier
  SIAM J Numer Anal 56(1), pp.160–184, 2018 [pdf ] [arxiv.org]

Published 2017

• Dictionary learning from incomplete data for efficient image restoration
  V. Naumova, K. Schnass
  25^th EUSIPCO Kos, pp.1425-1429, 2017 [pdf ] [arxiv.org]

• Iterative methods for photoacoustic tomography in attenuating acoustic media
  M. Haltmeier, R. Kowar, L. V. Nguyen
  Inverse Probl 33(11):115009, 2017 [pdf ] [arxiv.org]

• Inversion of the attenuated V-Line transform with vertices on the circle
  M. Haltmeier, S. Moon, D. Schiefeneder
  IEEE Trans Comput Imaging Vol. 3(4): 853-863, 2017 [arxiv.org]

• Low-rank approximation for FMCW automotive radar
  J. Sappl, P. Meissner, M. Haltmeier
  Proc. SampTA (IEEE), 2017

• Compressive time-of-flight imaging
  S. Antholzer, C. Wolf, M. Sandbichler, M. Dielacher, M. Haltmeier
  Proc. SampTA (IEEE), 2017

• Super-resolution photoacoustic microscopy using joint sparsity
  P. Burgholzer, M. Haltmeier, T. Berer, E. Leiss-Holzinger, T. W. Murray
  Proc. SPIE. 10415:1041506, 2017
  

• Nyström type subsampling analyzed as a regularized projection
  G. Kriukova, S. Pereverzyev Jr., P. Tkachenko
  Inverse Probl 33(7):074001, 2017 [pdf ]

• Multi-penalty regularization for detecting relevant variables
  K. Hlavackova-Schindler, V. Naumova, S. Pereverzyev Jr.
  Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science,889-916. Birkhäuser, 2017 [pdf ]

• Analytic inversion of a conical Radon transform arising in application of compton cameras on the cylinder
  S. Moon, M. Haltmeier
  SIAM J Imaging Sci 10(2), pp.535–557, 2017 [arxiv.org]

• The Radon transform over cones with vertices on the sphere and orthogonal axes
  D. Schiefeneder, M. Haltmeier
  SIAM  J Appl Math 77(4), pp.1335-1351, 2017 [arxiv.org]

• Analysis of iterative methods in photoacoustic tomography with variable sound speed
  M. Haltmeier, L. V. Nguyen
  SIAM J Imaging Sci 10(2), 2017 [arxiv.org]

• Compressed sensing in photoacoustic imaging and application for planar detection geometries
  T. Berer, P. Burgholzer, M. Haltmeier
  Proc. SPIE 10064:100642, 2017
  

• A parameter choice strategy for the inversion of multiple observations
  C. Gerhards, S. Pereverzyev Jr., P. Tkachenko
  Adv Comput Math 43(1), pp.101-112, 2017 [pdf ]

• The spherical Radon transform with centers on cylindrical surfaces
  M. Haltmeier, S. Moon
  J Math Anal Appl 448(1), pp.567-579,  2017 [arxiv.org]

• Super-resolution photoacoustic microscopy using blind structured illumination
  T. W. Murray, M. Haltmeier, T. Berer, E. Leiss-Holzinger and P. Burgholzer
  Optica 4(1), pp.17-22, 2017

Published 2016

• Compressed sensing and sparsity in photoacoustic tomography
  M. Haltmeier, T. Berer, S. Moon and P. Burgholzer
  J Opt 18(11), 2016

• Sampling conditions for the circular Radon transform
  M. Haltmeier
  IEEE Trans Image Process 25(6), pp.2910-2919, 2016 [pdf]

• Granger causality for Ill-Posed problems: ideas, methods, and application in life sciences
  K. Hlavackova-Schindler, V. Naumova, S. Pereverzyev Jr.
  Statistics and Causality: Methods for Applied Empirical Research, pp.249-276. Wiley, 2016.

• Sparsifying transformations of photoacoustic signals enabling compressed sensing algorithms
  P. Burgholzer, M. Sandbichler, F. Krahmer, T. Berer, M. Haltmeier
  Proc. SPIE 9708:970828, 2016

Published 2015

• A novel compressed sensing scheme for photoacoustic tomography
  M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, M. Haltmeier
  SIAM J Appl Math 75(6), pp.2475-2494, 2015 [pdf ]

• Mapping molecules in scanning far-field fluorescence nanoscopy
  T. Haisen, J. Keller, M. Haltmeier, S.K. Saka, J. Schmied, F. Opazo, P. Tinnefeld, A. Munk, S.W Hell
  Nat Commun, 6, 7977, 2015

• Aggregation of regularized solutions from multiple observation models
  J. Chen, S. Pereverzyev Jr., Y. Xu
  Inverse Probl 31(7):075005 , 2015 [pdf ]

• Single-stage reconstruction algorithm for quantitative photoacoustic tomography
  M. Haltmeier, L. Neumann, S. Rabanser
  Inverse Probl 31(6):065005, 2015 [pdf ]

• Causality analysis of the fractional state and wave equations based on fractional Laplacians
  R. Kowar
  Wave Motion 58, pp.244-258, 2015 [arxiv.org]
  

• The universal back-projection formula for spherical means and the wave equation on certain quadric hypersurfaces
  M. Haltmeier, S. Pereverzyev Jr.
  J Math Anal Appl 429(1), pp.366-382, 2015 [pdf ]

• Recovering a function from circular means or wave data on the boundary of parabolic domains
  M. Haltmeier, S. Pereverzyev Jr.
  SIAM J Imaging Sci 8(1), pp.592-610, 2015 [pdf ]

• Lasso Granger causal models: some strategies and their efficiency for gene expression regulatory networks
  K. Hlavackova-Schindler, S. Pereverzyev Jr.
  Chapter in T. v. Guy, M.Kárný, D. H. Wolpert (Editors): Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, 538, pp.91-117, Springer, 2015.
  

Published 2014

• Graphical Lasso Granger method with 2-levels-thresholding for recovering causality networks
  S. Pereverzyev Jr., K. Hlavackova-Schindler
  Chapter in C. Pötzsche, C. Heuberger, B. Kaltenbacher, F. Rendl: System Modeling and Optimization 443, pp.220-229, 2014 [pdf ]

• Introduction to the mathematics of computed tomography
  M. Haltmeier, S. Pereverzyev Jr.
  Internat Math Nachrichten 226, pp.29-51, 2014 [pdf ]

• Deblurring algorithms accounting for the finite detector size in photoacoustic tomography
  H. Roitner, M. Haltmeier, T. Berer, H. Grün, D.P. O'Leary, R. Nuster, G. Paltauf, P. Burgholzer
  J Biomed Opt 19(5):056011, 2014

• Exact reconstruction formula for the spherical Mean-Radon transform on ellipsoids
  M. Haltmeier
  Inverse Probl 30(10):105006, 2014 [pdf ]

• On time reversal in photoacoustic tomography for tissue similar to water
  R. Kowar
  SIAM J Imaging Sci 7(1), pp.509-527, 2014 [arxiv.org] [pdf ]

• Aggregated motion estimation for real-time MRI reconstruction
  H. Li, M. Haltmeier, S. Zhang, J. Frahm, A. Munk
  Magn Reson Med 72(4), pp.1039-1048, 2014 [arxiv.org]

• Time reversal for photoacoustic tomography based on the wave equation of Nachman, Smith and Waag.
  R. Kowar
  Phys Rev E Stat Nonlin Soft Matter Phys 89(2):023203, 2014, [arxiv.org] [pdf ]

• Extreme value analysis of empirical frame coefficients and implications for denoising by soft-thresholding
  M. Haltmeier, A. Munk
  Appl Comput Harmon Anal 36(3), pp.434-460 2014 [arxiv.org] [pdf ]

• Universal inversion formulas for recovering a function from spherical means
  M. Haltmeier
  SIAM J Math Anal 46(1), pp.214-232, 2014 [arxiv.org] [pdf ]

• Exact reconstruction formulas for a Radon transform over cones
  M. Haltmeier
  Inverse Probl 30(3):035001, 2014 [arixv.org] [pdf ]

• Parameter identification for an advanced material model for intact rock
  D. Unteregger, G. Hofstetter, M. Haltmeier, A. Ostermann
  Chapter 37 in M. A, Hicks, R. B, J, Brinkgreve, A. Rohe (Editors): Proc. NUMGE 2014, pp.215-220, 2014

• Spatial over-sampling and its influence on spatial resolution for photoacoustic tomography with finite sized detectors
  P. Burgholzer, H. Roitner, T. Berer, H. Grün, R. Nuster, G. Paltauf, M. Haltmeier
  Proc. SPIE 8943:89432K, 2014

• Credid scorecard for corporate clients based on industries
  S. Janicijevic, Z. Luzanin, S. Pereverzyev Jr., I. Stojkovska, A. Tepavcevic.
  Proc. 99^th European Study Group with Industry, 2014