Martin Holler

Publications

Preprints

M. Holler and E. Morina. On uniqueness in structured model learning. Submitted for publication, arXiv:2410.22009, 2024. arXiv
M. Holler and E. Morina. On the growth of the parameters of approximating ReLU neural networks. Submitted for publication, arXiv:2406.14936, 2024. arXiv

Edited Books

P. Grohs, M. Holler, and A. Weinmann, editors. Handbook of Variational Methods for Nonlinear Geometric Data. Springer, 2020.

Journal Articles

K. Bredies, M. Carioni, M. Holler, Y. Korolev and C.-B. Schönlieb. A sparse optimization approach to infinite infimal convolution regularization. To appear in Numerische Mathematik, 2024. arXiv
A. Habring and M. Holler. Neural-network-based regularization methods for inverse problems in imaging. Review paper. GAMM-Mitteillungen, 47(4):e202470004, 2024. Journal arXiv,
M. Holler, E. Morina and G. Schramm. Exact Parameter Identification in PET Pharmacokinetic Modeling Using the Irreversible Two Tissue Compartment Model.Physics in Medicine & Biology, 69(16):165008, 2024. Journal arXiv,
A. Habring, M. Holler and T. Pock. Subgradient Langevin Methods for Sampling from Non-smooth Potentials. SIAM Journal on Mathematics of Data Science, 6(4):897-925, 2024. Journal arXiv,
M. Holler, A. Schlüter and B. Wirth. Dimension reduction, exact recovery, and error estimates for sparse reconstruction in phase space. Applied and Computational Harmonic Analysis, 70:101631, 2024. Journal arXiv
M. Holler and B. Wirth. Exact reconstruction and reconstruction from noisy data with anisotropic total variation. SIAM Journal on Mathematical Analysis, 56(3):2938-2967, 2024. arXiv , SIAM
C. Aarset, A. Habring, M. Holler and M. Mitter. Unsupervised energy disaggregation via convolutional sparse coding. IEEE Transactions on Consumer Electronics, 70(1):4303-4310, 2024. arXiv , IEEE
D. Narnhofer, A. Habring, M. Holler and T. Pock. Posterior-variance-based error quantification for inverse problems in imaging. SIAM Journal on Imaging Sciences, 17(1):301-333, 2024. arXiv , SIAM
C. Aarset, M. Holler and T. T. N. Nguyen. Learning-informed parameter identification in nonlinear time-dependent PDEs. Applied Mathematics and Optimization, 88(76), 2023. Open Access
A. Habring and M. Holler. A Note on the Regularity of Images Generated by Convolutional Neural Networks. SIAM Journal on Mathematics of Data Science, 5(3):670-692, 2023. arXiv , SIAM
W. J. Kern, S. Orlob, A. Bohn, W. Toller, J. Wnent, J.-T. Gräsner and M. Holler. Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest. IEEE Transactions on Biomedical Engineering, 70(8):2310-2317, 2023. arXiv , IEEE
G. Schramm and M. Holler. Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET. Physics in Medicine and Biology, 67(15):155020, 2022. arXiv , IOP
K. Bredies, M. Carioni and M. Holler. Regularization Graphs -- A unified framework for variational regularization of inverse problems. Inverse Problems, 38(10):105006, 2022. Open Access
S. Orlob, W. Kern, B. Alpers, M. Schörghuber, A. Bohn, M. Holler, J-T. Gräsner and J. Wnent. Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data - tested in Zoll X Series. Resuscitation, 172:162-169, 2022. Open Access
A. Habring, M. Holler. A Generative Variational Model for Inverse Problems in Imaging. SIAM Journal on Mathematics of Data Science 4(1):306-335, 2022. arXiv , SIAM
K. Bredies, M. Holler. Higher-order total variation approaches and generalisations. Topical Review. Inverse Problems, 36(12):123001, 2020. Open Access
A. Chambolle, M. Holler and T. Pock. A convex variational model for learning convolutional image atoms from incomplete data. Journal of Mathematical Imaging and Vision: Special Issue on Mathematics of Deep Learning, 62:417-444, 2020. Open Access
R. Huber, G. Haberfehlner, M. Holler, G. Kothleitner and K. Bredies. Total Generalized Variation regularization for multi-modal electron tomography. Nanoscale, 11:5617-5632, 2019. Open Access
A. Lesch, M. Schlögl, M. Holler, K. Bredies and R. Stollberger. Ultrafast 3D Bloch-Siegert B₁⁺-mapping using variational modeling. Magnetic Resonance in Medicine, 81(2):881-892, 2019. Open Access
M. Holler, R. Huber and F. Knoll. Coupled regularization with multiple data discrepancies. Inverse Problems, 34(8):084003, 2018. Open Access
K. Bredies, M. Holler, M. Storath and A. Weinmann. Total Generalized Variation for manifold-valued data. SIAM Journal on Imaging Sciences, 11(3):1785-1848, 2018. PDF , SIAM
M. Hintermüller, M. Holler and K. Papafitsoros. A function space framework for structural total variation regularization with applications in inverse problems. Inverse Problems, 34(6):064002, 2018. Open Access
M. Holler and K. S. Kazimierski. Variational decompression of image data from DjVu encoded files. IEEE Transactions on Image Processing, 27(1):490-499, 2018. PDF
G. Schramm, M. Holler, A. Rezaei, K. Vunckx, F. Knoll, K. Bredies, F. Boada and J. Nuyts. Evaluation of parallel level sets and Bowsher's method as segmentation-free anatomical priors for time-of-flight PET reconstruction. IEEE Transactions on Medical Imaging, 37(2):590-603, 2018. PDF , IEEE
F. Knoll, M. Holler, T. Koesters, R. Otazo, K. Bredies and D. K. Sodickson. Joint MR-PET reconstruction using a multi-channel image regularizer. IEEE Transactions on Medical Imaging, 36(1):1-16, 2017. PDF
M. Schloegl, M. Holler, A. Schwarzl, K. Bredies and R. Stollberger. Infimal convolution of Total Generalized Variation functionals for dynamic MRI. Magnetic Resonance in Medicine, 78(1):142-155, 2017. PDF Open Access
A. Crozier, C. M. Augustin, A. Neic, A. J. Prassl, M. Holler, T. E. Fastl, A. Hennemuth, K. Bredies, T. Kuehne, M. J. Bishop, S. A. Niederer and G. Plank. Image-based personalization of cardiac anatomy for coupled electromechanical modeling. Annals of Biomedical Engineering, 44(1):58-70, 2016. Open Access
K. Bredies and M. Holler. A TGV-based framework for variational image decompression, zooming and reconstruction. Part I: Analytics. SIAM Journal on Imaging Sciences, 8(4):2814-2850, 2015. PDF
K. Bredies and M. Holler. A TGV-based framework for variational image decompression, zooming and reconstruction. Part II: Numerics. SIAM Journal on Imaging Sciences, 8(4):2851-2886, 2015. PDF
M. Holler and K. Kunisch. On infimal convolution of TV type functionals and applications to video and image reconstruction. SIAM Journal on Imaging Sciences, 7(4):2258-2300, 2014. PDF
K. Bredies and M. Holler. Regularization of linear inverse problems with total generalized variation. Journal of Inverse and Ill-posed Problems, 22(6):871-913, 2014. PDF
K. Bredies and M. Holler. A Total-Variation-based JPEG decompression model. SIAM Journal on Imaging Sciences, 5(1):366-393, 2012. PDF

Book Chapters

M. Holler and A. Weinmann. Non-smooth regularization for manifold-valued data. In P. Grohs, M. Holler, and A. Weinmann, editors, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, 2020. PDF

Peer-Reviewed Proceedings Papers

A. Abdullah, M. Holler, K. Kunisch and M. Sabate Landman. Latent-Space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data. In: Scale Space and Variational Methods in Computer Vision, Springer LNCS, 14009:326-338, 2023. arXiv , Springer
G. Schramm, M. Holler, T. Koesters, F. Boada, F. Knoll, K. Bredies, and J. Nuyts. PET reconstruction with non-smooth gradient-based priors. In: Proceedings of the 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD), 1-5, 2017. IEEE
F. Knoll, M. Holler, T. Koesters, K. Bredies, and D. K. Sodickson. Simultaneous PET-MRI reconstruction with vectorial second order total generalized variation. In: Proceedings of the 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 1-4, 2016. IEEE
R. Huber, M. Holler, and K. Bredies. Graph-Laplacian minimisation for surface smoothing in 3D finite element tetrahedral meshes. In: Proceedings of the 1st OAGM-ARW Joint Workshop -- Vision Meets Robotics, 53-60, 2016. PDF
K. Bredies and M. Holler. Artifact-free variational MPEG decompression. Scale Space and Variational Methods in Computer Vision, Springer LNCS, 9087:216-228, 2015. PDF
K. Bredies and M. Holler. Artifact-free decompression and zooming of JPEG compressed images with total generalized variation. Computer Vision, Imaging and Computer Graphics. Theory and Application, Springer CCIS, 359:242-258, 2013. PDF
K. Bredies and M. Holler. A TGV regularized wavelet based zooming model. Scale Space and Variational Methods in Computer Vision, Springer LNCS, 7893:149-160, 2013. PDF
K. Bredies and M. Holler. Artifact-free JPEG decompression with total generalized variation. Proceedings of VISAPP 2012 - International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 12-21, 2012. PDF

Theses

M. Holler. Higher order regularization for model based data decompression. PHD Thesis, University of Graz, 2013 PDF
M. Holler. Theory and numerics for varational imaging. Artifact-free JPEG decompression and DCT based zooming. Masters Thesis, University of Graz, 2010. PDF

Unpublished

K. Bredies and M. Holler. A pointwise characterization of the subdifferential of the total variation functional. arXiv:1609.08918, 2016. PDF