F. Beier, H. Bi, C. Sarazzin, B. Schmitzer and G. Steidl, Transfer operators from batches of unpaired points via entropic kernel transport, ArXiv preprint 2402.08425, 2024.

J. Chemseddine, P. Hagemann, C. Wald and G. Steidl, Conditional Wasserstein distances with applications in Bayesian ot ﬂow matching, ArXiv preprint 2403.18705, 2024.

P. Hagemann, J. Hertrich, M. Castor, S. Heidenreich and G. Steidl, Mixed noise and posterior estimation with condiional deepGEM, ArXiv preprint 2402.02964, 2024.

M. Hanik, G. Steidl and C. von Tycowicz, Manifold GCN: Diﬀusion based convolutional neural network for manifold-valued graphs, ArXiv preprint 2401.14381, 2024.

O. Melnyk, M. Quellmalz, G. Steidl, N. Jaitner, J. Jordan and I. Sack, Time-harmonic optical ﬂow and ist application in elastography, ArXiv preprint 2403.18705, 2024.

S. Neumayer, V. Stein and G. Steidl, Wasserstein gradient ﬂows of Moreau envelopes of f-divergences in reproducing kernel Hilbert spaces, ArXiv preprint 2402.02964, 2024.

M. Piening, F. Altkrüger, J. Hertrich, P. Hagemann, A. Walther and G. Steidl, Learning from small data sets: Patch-based regularizers in inverse problems for image reconstrucion, ArXiv preprint 2312.16611, 2024.

J. von Lindheim and G. Steidl, Generalized iterative scaling for regularized optimal transport with aﬃne constraints: Application examples, ArXiv preprint 2305.07071, 2023.

P.Hagemann, S. Mildenberger, L. Ruthotto, G. Steidl and N. Yang, Multilevei diﬀusion: Inﬁnite dimensional score-based diﬀusion models for image generation, ArXiv preprint 230.40772, 2023.

C. Wald and G. Steidl, A study of particle motion in the presence of clusers, ArXiv preprint 2308.11314, 2023.

G. Steidl, Space, space, 2024.

F. Altekrüger, P. Hagemann and G. Steidl, Conditional generative models are provably robust: Pointwise guarantees for Bayesian inverse problems, Transactions on Machine Learning Research (TMLR), 2024.

F. Ba, O. Melnyk, G. Steidl and C. Wald, Sparse adaptive function decomposition facing basis transforms, Foundations in Data Science, 2024.

R. Beinert, J. Bresch and G. Steidl, Denoising of sphere and SO(3)-valued data by relaxed Tikhonov regularization, Inverse Problems and Imaging, 2024.

A. Berdellima and G. Steidl, Quasi-alpha ﬁrmly nonexpansive mappings in Wasserstein spaces, Fixed Point Theory, 2024.

P. Hagemann, J. Hertrich, F. Altekrüger, R. Beinert, J. Chenseddine and G. Steidl, Posterior sampling based on gradient ﬂows of the MMD with negative disance kernel, International Conference on Machine Learning Research (ICLR), 2024.

J. Hertrich, R. Beinert, M. Gräf and G. Steidl, Wasserstein steepest descent ﬂow of discrepancies with Riesz kernels, Journal of Mathematical Analysis and Applicaions, vol. 531, p. 127 829, 2024.

M. Quellmalz, L. Buecher and G. Steidl, Parallely sliced opimal transport on spheres and he rotation group, Journal of Mathematical Imaging and Vision, 2024.

M. Quellmalz, P. Elbau, O. Scherzer and G. Steidl, Motion detection by diﬀraction tomography by common circle methods, Mathematics of Computation, vol. 94, no. 346, pp. 747–784, 2024.

F. Altekrueger, A. Denker, P. Hagemann, J. Hertrich, P. Maas and G. Steidl, PatchNR: Learning from small data by patch normalizing ﬂow regularization, Inverse Problems, vol. 39, no. 6, 2023.

F. Altekrüger, J. Hertrich and G. Steidl, Neural Wasserstein gradient ﬂows for discrepancies wih Riesz kernels, Internaional Conference on Machine Learning (ICML), vol. 202, pp. 664–690, 2023.

F. Beier, R. Beinert and G. Steidl, Multimarginal Gromov-Wasserstein transport and barycenters, Information and Inference: A Journal of he IMA, vol. 14, no. 4, pp. 2753–2781, 2023.

F. Beier, J. von Lindheim, S. Neumayer and G. Steidl, Unbalanced multimarginal optimal transport, Journal of Mathematical Imaging and Vision, vol. 65, no. 3, pp. 394–413, 2023.

R. Beinert, C. Heiss and G. Steidl, On assignment problems related to Gromov-Wasserstein distances on the real line, SIAM J. Imaging Sciences, vol. 16, no. 2, pp. 1028–1032, 2023.

R. Beinert, G. Steidl and M. Quellmalz, Sliced optimal transport on the sphere, Inverse Problems, 2023.

P. Hagemann, J. Hertrich and G. Steidl, Generalized Normalizing Flows via Markov Chains. Cambridge University Press, 2023.

C. Heiss, P. Flotho, G. Steidl and D. Strauss, Lagrangian motion magniﬁcation with double sparse optical ﬂow decomposition, Frontiers in Applied Mathematics and Statistics, 2023.

J. Hertrich, R. Beinert, M. Gräf and G. Steidl, Wasserstein gradient ﬂows of the discrepancy with distance kernel on the line, Scale Space and Variational Methods (SSVM), vol. LNCS 14009, pp. 431–443, 2023.

M. Liesegang, M. Gräf, T. Beck and G. Steidl, Investigation for the tensile deformation behaviour in Ni based super alloy 617 using EBSD-based ﬁnite elemen simulations and optical ﬂow methods, Journal of Material Science, 2023.

S. Petra, C. Schnörr, G. Steidl and M. Zisler, On the remarkable eﬃciency of SMART, Scale Space and Variational Methods (SSVM), vol. LNCS 14009, 2023.

F. Beier, R. Beinert and G. Steidl, On a linear Gromov-Wasserstein distance, IEEE Trans. Image Processing, vol. 31, pp. 7292–7305, 2022.

A. Berdellima, R. Beinert, M. Gräf and G. Steidl, On the dynamical system of principal curves in Rd, Communications in Statistic: Simulation and Computation, vol. 12, pp. 1–20, 2022.

S. Dahlke, F. D. Mari, E. D. Vito, M. Hansen, M. Hasannasab, M. Quellmalz, G. Steidl and G. Teschke, Continuous wavelet frames on the sphere: The group-theoretic approach revisited, Applied and Computational Harmonic Analysis, vol. 56, pp. 123–149, 2022.

M. Gräf, S. Neumayer, R. Hielscher, G. Steidl, M. Liesegang and T. Beck, An optical ﬂow model in electron backscatter diﬀraction, SIAM Journal on Imaging Sciences, vol. 15, no. 1, pp. 228–260, 2022.

C. Heiss, P. Flotho, G. Steidl and D. Strauss, Lagrangian motion magniﬁcation with landmark prior and sparse PCA for facial microexpressions and micromovements, 44th Annual Internat. Conf. IEEE Engineering in Medicine and Biology Soc., 2022.

J. Hertrich, F. Ba and G. Steidl, Sparse ANOVA inspired mixture models, ETNA, vol. 55, pp. 142–168, 2022.

J. Hertrich, P. Hagemann and G. Steidl, Stochastic normalizing ﬂows for inverse problems, a Markov chain viewpoint, SIAM Journal on Uncertainty Quantiﬁcation, vol. 10, no. 3, pp. 1162–1190, 2022.

J. Hertrich and G. Steidl, Inertial stochastic PALM and its application for learning Student-t mixture models, Sampling Theory, Signal Processing and Data Analysis, vol. 20, no. 1, pp. 1–32, 2022.

J. Hertrich, D. P. L. Nguyen, J.-F. Aujol, D. Bernard, Y. Berthoumieu, A. Saadaldin and G. Steidl, PCA reduced Gaussian mixture models with application in superresolution, Inverse Problems and Imaging, vol. 16, pp. 341–366, 2022.

A. Andrele, N. Fachmin, P. Hagemann, V. Soltewisch and G. Steidl, Invertible neural networks versus mcmc for posterior reconstruction in gazing incidence X-ray ﬂuorescence, in Scale Space and Variational Methods in Computer Vision, A. Elmoataz, J. fadili, Y. Quéau, R. Rabin and L. Simon, Eds., ser. LNCS 12679, 2021, pp. 528–539.

R. Beinert, P. Jung, G. Steidl and T. Szollmann, Superresolution for doubly-dispersive channel estimation, Samplin Theory, Signal Processing and Data Analysis, vol. 19, no. 16, pp. 1–36, 2021.

R. Beinert and G. Steidl, Robust PCA via regularized reaper with a matrix-free proximal algorithm, Journal of Mathematical Imaging and Vision, vol. 63, pp. 626–649, 2021.

A. Berdellima and G. Steidl, On alpha-ﬁrmly nonexpansive operators in r-uniformly convex spaces, Results in Mathematics, vol. 76, no. 4, pp. 1–27, 2021.

O. Christensen, M. Hasannasab and G. Steidl, On approximate operator representations of sequences in Banach spaces, Complex Analysis and Operator Theory, vol. 15, no. 3, 2021.

M. Ehler, M. Gräf, S. Neumayer and G. Steidl, Curve based approximation of measures on manifolds by discrepancy minimization, Foundations in Computational Mathematics, vol. 21, pp. 1595–1642, 2021.

C. Hartman, H. A. Weiss, P. Lechner, W. Volk, S. Neumayer, J. H. Fitschen and G. Steidl, Measurement of strain, strain rate and crack evolution in shear cutting, Journal of Materials Processing Technology, vol. 288, p. 116 872, 2021.

M. Hasanasab, J. Hertrich, F. Laus and G. Steidl, Alternatives of the EM algorithm for estimating the parameters of the Student-t distribution, Numerical Algorithms, vol. 87, pp. 77–118, 2021.

J. Hertrich, S. Neumayer and G. Steidl, Convolutional proximal neural networks and plug-and-play algorithms, Linear Algebra and its Applications, vol. 631, pp. 203–234, 2021.

C. Kirisits, M. Quellmalz, M. Ritsch-Marte, O. Scherzer, E. Setterqvist and G. Steidl, Fourier reconstruction for diﬀraction tomography of an object rotated into arbitrary orientations, Inverse Problems, vol. 37, no. 11, p. 115 002, 2021.

P. Koltai, J. von Lindheim, S. Neumayer and G. Steidl, Transfer operators from optimal transport plans for coherent set, Physica D: Nonlinear Phenomena, vol. 426, No 132980, 2021.

S. Neumayer and G. Steidl, From optimal transport to discrepancy, in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, Springer, 2021.

C. B. Schoenlieb, H. Zhao, G. Steidl and M. B. Watkin, Principles and trends in mathematical imaging, SIAM News, vol. March, 2021.

M. Bacak, J. Hertrich, S. Neumayer and G. Steidl, Minimal Lipschitz and inftinity–harmonic extensions of vector-valued functions on ﬁnite graphs, Information and Inference: A Journal of the IMA, vol. 9, no. 4, pp. 935–959, 2020.

T. Batard, J. Hertrich and G. Steidl, Variational models for color image correction inspired by visual perception and neuroscience, Journal of Mathematical Imaging and Vision, vol. 62, no. 9, pp. 1173–1194, 2020.

R. H. Chan, A. Cohen, J. Fadili, A. Hero and G. Steidl, Guest editorial: Special issue in memory of mila nikolova, Journal of Mathematical Imaging and Vision, vol. 62, 2020.

M. Hasannasab, J. Hertrich, S. Neumayer, G. Plonka, S. Setzer and G. Steidl, Parseval proximal neural networks, The Journal of Fourier Analysis and its Applications, vol. 26, pp. 1–31, 2020.

D. Havenstein, P. Lysakovski, N. May, G. Moerkotte and G. Steidl, Fast entropy maximization for selectivity estimation of conjunctive predicates on CPUs and GPUs, in Proceedings of the 23rd International Conference on Extending Database Technology (EDBT), 2020, pp. 1–9.

S. Neumayer, M. Nimmer, S. Setzer and G. Steidl, On the robust PCA and Weiszfeld’s algorithm, Applied Mathematics and Optimization, vol. 82, pp. 1017–1048, 2020.

S. Neumayer, M. Nimmer, S. Setzer and G. Steidl, On the rotational invariant l1-norm PCA, Linear Algebra and its Applications, vol. 587, pp. 243–270, 2020.

G. Steidl and M. Winkler, A new constrained model for solving the nonsymmetric inverse stochastic eigenvalue problem, Linear and Multilinear Algebra, pp. 1–30, 2020.

J.-F. Aujol, J. Fadili, M. Hintermüller, G. Peyré, G. Plonka-Hoch and G. Steidl, Guest Editorial JMIV Special Issue Mathematics and Image Analysis (MIA), J. Math. Imaging Vis., vol. 61, no. 5, 2019.

F. Balle, T. Beck, D. Eiﬂer, J. H. Fitschen, S. Schuﬀ and G. Steidl, Strain analysis by a total generalized variation regularized optical ﬂow model, Inverse Problems in Science and Engineering, vol. 27, no. 4, 2019.

T. Batard, M. Bertalmio and G. Steidl, A connection between image processing and artiﬁcial neural network layers through a geometric model of visual perception, in Scale Space and Variational Methods in Computer Vision, M. Burger, J. Lellmann and J. Modersitzki, Eds., ser. LNCS, 2019.

R. Bergmann, F. Laus, J. Persch and G. Steidl, Recent advances in denoising of manifold-valued images, in Handbook of Numerical Analysis, R. Kimmel and X.-C. Tai, Eds., vol. HNA 20, Elsevier, 2019, pp. 553–578.

X. Cai, R. Chan, C.-B. Schönlieb, G. Steidl and T. Zeng, Linkage between piecewise constand Mumford-Shah model and ROF model and its virtue in image segmentation, SIAM Journal on Scientiﬁc Computing, vol. 41, no. 6, pp. 1310–1340, 2019.

S. Dahlke, Q. Jahan, C. Schneider, G. Steidl and G. Teschke, Traces of shearlet coorbit spaces on domains, Applied Mathematics Letters, vol. 91, pp. 35–40, 2019.

S. Dahlke, F. D. Mari, E. D. Vito, L. Sawatzki, G. Steidl, G. Teschke and F. Voigtlaender, On the atomic decomposition of coorbit spaces with non-integrable kernel, in Landscapes of Time-Frequency Analysis, P. Boggiatto, E. Cordero, M. de Gosson, H. G. Feichtinger, F. Nicola, A. Oliaro and A. Tabacco, Eds., Birkhäuser, 2019, pp. 75–144.

D. Dobrovolskij, J. Persch, K. Schladitz and G. Steidl, Structure detection with second order Riesz transforms, Image Analysis & Stereology, vol. 38, no. 1, pp. 107–119, 2019.

J. Hertrich, M. Bačák, S. Neumayer and G. Steidl, Minimal Lipschitz extensions for vector-valued functions on ﬁnite graphs, in Scale Space and Variational Methods in Computer Vision, M. Burger, J. Lellmann and J. Modersitzki, Eds., ser. LNCS, 2019.

F. Laus and G. Steidl, Multivariate myriad ﬁlters based on parameter estimation of Student-t distributions, SIAM Journal on Imaging Sciences, vol. 12, no. 4, pp. 1864–1904, 2019.

J. Lellmann, S. Neumayer, M. Nimmer and G. Steidl, Methods for ﬁnding the oﬀset in robust space ﬁtting, PAMM, vol. 19, no. 1, e201900323, 2019.

S. Neumayer, J. Persch and G. Steidl, Regularization of inverse problems via time discrete geodesics in image spaces, Inverse Problems, vol. 35, no. 5, 2019.

R. Bergmann, J. H. Fitschen, J. Persch and G. Steidl, Priors with coupled ﬁrst and second order diﬀerences for manifold-valued image processing, Journal of Mathematical Imaging and Vision, vol. 60, no. 9, pp. 1459–1481, 2018.

F. Laus, F. Pierre and G. Steidl, Nonlocal myriad ﬁlters for Cauchy noise removal, Journal of Mathematical Imaging and Vision, vol. 60, no. 8, pp. 1324–1354, 2018.

S. Neumayer, J. Persch and G. Steidl, Morphing of manifold-valued images inspired by discrete geodesics in image spaces, SIAM Journal of Imaging Sciences, vol. 11, no. 3, pp. 1898–1930, 2018.

M. Nimmer, G. Steidl, R. Riesenberg and A. Wuttig, Spectral imaging based on 2D diﬀraction patterns and a regularization model, Optics Express, vol. 26, no. 22, pp. 28 335–28 348, 2018.

G. Plonka, D. Potts, G. Steidl and M. Tasche, Numerical Fourier Analysis (Applied and Numerical Harmonic Analysis). Birkhäuser, 2018.

M. Bačák, M. Montag and G. Steidl, Convergence of functions and their Moreau envelopes on Hadamard spaces, Journal of Approximation Theory, vol. 224, pp. 1–12, 2017.

B. Bauer, X. Cai, S. Peth, K. Schladitz and G. Steidl, Variational-based segmentation of biopores in tomographic images, Computers & Geosciences, vol. 98, pp. 1–8, 2017.

R. Bergmann, J. H. Fitschen, J. Persch and G. Steidl, Iterative multiplicative ﬁlters for data labeling, International Journal of Computer Vision, vol. 123, no. 3, pp. 123–145, 2017.

R. Bergmann, F. Laus, J. Persch and G. Steidl, Processing manifold-valued images, Siam News, vol. 50, no. 8, pp. 1, 3, 2017.

M. Burger, A. Sawatzky and G. Steidl, First order algorithms in variational image processing, in First order algorithms in variational image processing, R. Glowinski, S. Osher and W. Yin, Eds., Springer, 2017.

S. Dahlke, F. D. Mari, E. D. Vito, D. Labate, G. Steidl, G. Teschke and S. Vigogna, Coorbit spaces with voice in a Fréchet space, The Journal of Fourier Analysis and its Applications, vol. 23, no. 1, pp. 141–206, 2017.

J. Fadili, K. G, G. Peyré, G. Plonka-Hoch and G. Steidl, Guest Editorial JMIV Special Issue Mathematics and Image Analysis (MIA), J. Math. Imaging Vis., vol. 59, no. 3, 2017.

J. H. Fitschen, K. Losch and G. Steidl, Unsupervised multi class segmentation of 3D images with intensity inhomogeneities, Journal of Visual Communication and Image Representation, vol. 46, pp. 312–323, 2017.

J. H. Fitschen, J. Ma and S. Schuﬀ, Removal of curtaining eﬀects by a variational model with directional forward diﬀerences, Computer Vision and Image Understanding, vol. 155, pp. 24–32, 2017.

F. Laus, M. Nikolova, J. Persch and G. Steidl, A nonlocal denoising algorithm for manifold-valued images using second order statistics, SIAM Journal on Imaging Sciences, vol. 10, no. 1, pp. 416–448, 2017.

T. H. Loeber, B. Laegel, S. Wolﬀ, S. Schuﬀ, F. Balle, T. Beck, D. Eiﬂer, J. H. Fitschen and G. Steidl, Reducing curtaining eﬀects in FIB/SEM applications by a goniometer stage and an image processing method, Journal of Vacuum Science & Technology B, vol. 35, no. 6, 2017.

S. Neumayer, M. Nimmer and G. Steidl, On a projected Weiszfeld algorithm, in Scale Space and Variational Methods in Computer Vision, F. Lauze, Y. Dong and A. B. Dahl, Eds., ser. LNCS, vol. 10302, 2017, pp. 486–497.

J. Persch, F. Pierre and G. Steidl, Exemplar-based face colorization using image morphing, Journal of Imaging, vol. 3, no. 4, 2017, Art. No. 48.

F. Pierre, J.-F. Aujol, A. Bugeau, S. Steidl and V.-R. Ta, Variational contrast enhancement of gray-scale and RGB images, Journal of Mathematical Imaging and Vision, vol. 57, no. 1, pp. 99–116, 2017.

M. Bačák, R. Bergmann, G. Steidl and A. Weinmann, A second order non-smooth variational model for restoring manifold-valued images, SIAM Journal on Scientiﬁc Computing, vol. 38, no. 1, pp. 567–597, 2016.

R. Bergmann, R. H. Chan, R. Hielscher, J. Persch and G. Steidl, Restoration of manifold-valued images by half-quadratic minimization, Inverse Problems and Imaging, vol. 10, no. 2, pp. 281–304, 2016.

R. Bergmann, J. Persch and G. Steidl, A parallel Douglas-Rachford algorithm for minimizing ROF-like functionals on images with values in symmetric Hadamard manifolds, SIAM Journal on Imaging Sciences, vol. 9, no. 3, pp. 901–937, 2016.

S. Dahlke, F. D. Mari, E. D. Vito, S. Häuser, G. Steidl and G. Teschke, Diﬀerent faces of the shearlet group, The Journal of Geometric Analysis, vol. 26, no. 3, pp. 1693–1729, 2016.

J. H. Fitschen, F. Laus and G. Steidl, Transport between RGB images motivated by dynamic optimal transport, Journal of Mathematical Imaging and Vision, vol. 56, no. 3, pp. 409–429, 2016.

F. Pierre, J.-F. Aujol, A. Bugeau, G. Steidl and V. T. Ta, Hue-preserving perceptual contrast enhancement, Proc. International Conference on Image Processing (ICIP) 2016, 2016, pp. 1–5.

F. Balle, D. Eiﬂer, J. H. Fitschen, S. Schuﬀ and G. Steidl, Computation and visualization of local deformation for multiphase metallic materials by inﬁmal convolution of TV-type functionals, in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova and N. Papadakis, Eds., ser. LNCS, vol. 9087, 2015, pp. 385–396.

X. Cai, J. H. Fitschen, M. Nikolova, G. Steidl and M. Storath, Disparity and optical ﬂow partitioning using extended potts priors, MA Journal of Information and Inference, vol. 4, no. 1, pp. 43–62, 2015.

S. Dahlke, S. Häuser, G. Steidl and G. Teschke, Shearlet coorbit spaces: Traces and embeddings in higher dimensions, Monatshefte für Mathematik, vol. 169, no. 1, pp. 15–32, 2015.

S. Dahlke, S. Häuser, G. Steidl and G. Teschke, Shearlet coorbit theory, in Harmonic and Applied Analysis, S. Dahlke, F. DeMari, P. Grohs and D. Labate, Eds., Birkhäuser, 2015, pp. 83–147.

J. Fadili, K. G, G. Peyré, G. Plonka-Hoch and G. Steidl, Guest Editorial JMIV Special Issue Mathematics and Image Analysis (MIA), J. Math. Imaging Vis., vol. 52, no. 3, 2015.

J. Fehrenbach, M. Nikolova, G. Steidl and P. Weiss, Bilevel image denoising using Gaussianity tests, in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova and N. Papadakis, Eds., ser. LNCS, vol. 9087, 2015, pp. 117–128.

J. H. Fitschen, F. Laus and G. Steidl, Dynamic optimal transport with mixed boundary condition for color image processing, in International Conference on Sampling Theory and Applications (SampTA), 2015, 2015, pp. 558–562.

J. H. Fitschen, M. Nikolova, F. Pierre and G. Steidl, A variational model for color assignment, in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova and N. Papadakis, Eds., ser. LNCS, vol. 9087, 2015, pp. 437–448.

G. Moerkotte, M. Montag, A. Repetti and G. Steidl, Proximal operator of quotient functions with application to a feasibility problem in query optimization, Journal of Computational and Applied Mathematics, vol. 285, pp. 243–255, 2015.

Z. Mortezapouraghdam, L. Haab, F. I. Corona-Strauss, G. Steidl and D. J. Strauss, Assessment of long-term habituation correlates in event-related potentials using a von mises model, IEEE Transactions on Neural Systems & Rehabilitation Engineering, pp. 363–373, 2015.

G. Steidl, Combined ﬁrst and second order variational approaches for image processing, Jahresbericht der Deutschen Mathematiker-Vereinigung 2015, vol. 117, no. 2, pp. 133–160, 2015.

F. Baus, M. Nikolova and G. Steidl, Fully smoothed l1-TV models: Bounds for the minimizers and parameter choice, Journal of Mathematical Imaging and Vision, vol. 48, no. 2, pp. 295–307, 2014.

R. Bergmann, F. Laus, G. Steidl and A. Weinmann, Second order diﬀerences of cyclic data and applications in variational denoising, SIAM Journal on Imaging Sciences, vol. 7, no. 4, pp. 2916–2953, 2014.

S. Häuser, B. Heise and G. Steidl, Linearized Riesz transform and quasi-monogenic shearlets, International Journal of Wavelets, Multiresolution and Information Processing, vol. 12, no. 3, p. 1 450 027, 2014.

S. H. Kang, B. Shafei and G. Steidl, Supervised and transductive multi-class segmentation using p-Laplacians and RKHS methods, J. Visual Communication and Image Representation, vol. 25, no. 5, pp. 1136–1148, 2014.

G. Kutyniok, W. Lim and G. Steidl, Shearlets: Theory and applications, GAMM-Mitteilungen, vol. 37, no. 2, pp. 259–280, 2014.

A. Liebscher, J. Meinhardt, A. Rack, K. Schladitz, B. Shafei, G. Steidl and O. Wirjadi, Microstructural analysis of a C/SiC ceramic based on the segmentation of 3D image data, International Journal of Materials Research, vol. 105, no. 7, pp. 702–708, 2014.

M. Nikolova and G. Steidl, Fast hue and range preserving histogram speciﬁcation: Theory and new algorithms for color image enhancement, IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 4087–4100, 2014.

M. Nikolova and G. Steidl, Fast ordering algorithm for exact histogram speciﬁcation, IEEE Transactions on Image Processing, vol. 23, no. 12, pp. 5274–5283, 2014.

J. K. Schubert, E. Gonzalez-Trejo, W. Retz, M. Rösler, F. I. Corona-Strauss, G. Steidl, T. Teuber and D. J. Strauss, Dysfunctional cortical inhibition in adult ADHD: Neural correlates in auditory event-related potentials, Journal of Neuroscience Methods, vol. 235, pp. 181–188, 2014.

X. Cai and G. Steidl, Multiclass segmentation by iterated ROF thresholding, in Energy Minimization Methods in Computer Vision and Pattern Recognition, F. Kahl, A. Heyden, C. Olsson, M. Oskarsson and C.-C. Tai, Eds., ser. LNCS, Berlin: Springer, 2013, pp. 237–250.

R. Ciak, B. Shafei and G. Steidl, Homogeneous penalizers and constraints in convex image restoration, Journal of Mathematical Imaging and Vision, vol. 47, no. 3, pp. 210–230, 2013.

M. Fornasier, J. Haskovec and G. Steidl, Consistency of variational continuous-domain quantization via kinetic theory, Applied Analysis, vol. 92, no. 6, pp. 1283–1298, 2013.

S. Harizanov, J.-C. Pesquet and G. Steidl, Epigraphical projection for solving least squares Anscombe transformed constrained optimization problems, in Scale Space and Variational Methods in Computer Vision, A. Kuijper, K. Bredies, T. Pock and H. Bischof, Eds., ser. LNCS, vol. 7893, Berlin: Springer, 2013, pp. 125–136.

S. Häuser and G. Steidl, Convex multiclass segmentation with shearlet regularization, International Journal of Computer Mathematics, vol. 90, no. 1, pp. 62–81, 2013.

S. Setzer, G. Steidl and J. Morgenthaler, A cyclic projected gradient method, Computational Optimization and Applications, vol. 54, no. 2, pp. 417–440, 2013.

D. J. Strauss, T. Teuber, G. Steidl and F. I. Corona-Strauss, Exploiting the self-similarity in ERP images by nonlocal means for single-trial denoising, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 4, pp. 576–583, 2013.

T. Teuber, G. Steidl and R. H. Chan, Minimization and parameter estimation for seminorm regularization models with I-divergence constraints, Inverse Problems, vol. 29, pp. 1–28, 2013.

S. Dahlke, G. Steidl and G. Teschke, Multivariate shearlet transform, shearlet coorbit spaces and their structural properties, in Shearlets, Multiscale Analysis for Multivariate Data, G. Kutyniok and D. Labate, Eds., Birkhäuser, 2012, pp. 105–144.

M. Gräf, D. Potts and G. Steidl, Quadrature nodes meet stippling dots, in Proceedings SSVM 2011, A. M. Bruckstein, B. M. ter Haar Romney, A. M. Bronstein and M. M. Bronstein, Eds., Springer, 2012, pp. 568–580.

M. Gräf, D. Potts and G. Steidl, Quadrature rules, discrepancies and their relations to halftoning on the torus and the sphere, SIAM Journal on Scientiﬁc Computing, vol. 34, no. 5, pp. 2760–2791, 2012.

Y. He, B. Shafei, M. Y. Hussaini, J. Ma and G. Steidl, A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data, Pattern Recognition, vol. 45, pp. 3436–3471, 2012.

S. Setzer, G. Steidl and T. Teuber, On vector and matrix median computation, Journal of Computational and Applied Mathematics, vol. 236, pp. 2200–2222, 2012.

B. Shafei and G. Steidl, Segmentation of images with separating layers by fuzzy c-means and convex optimization, J. Visual Communication and Image Representation, vol. 23, pp. 611–621, 2012.

T. Teuber, S. Remmele, J. Hesser and G. Steidl, Denoising by second order statistics, Signal Processing, vol. 92, no. 12, pp. 2837–2847, 2012.

S. Dahlke, G. Steidl and G. Teschke, Shearlet coorbit spaces: Compactly supported analyzing shearlets, traces and embeddings, The Journal of Fourier Analysis and its Applications, vol. 17, no. 6, pp. 1232–1255, 2011.

S. Setzer, G. Steidl and T. Teuber, Inﬁmal convolution regularizations with discrete l1-type functionals, Communications in Mathematical Sciences, vol. 9, no. 3, pp. 797–872, 2011.

G. Steidl, Supervised learning by support vector machines, in Handbook of Mathematical Methods in Imaging, O. Scherzer, Ed., Springer, 2011, pp. 959–1014.

T. Teuber, G. Steidl, P. Gwosdek, C. Schmaltz and J. Weickert, Dithering by diﬀerences of convex functions, SIAM Journal on Imaging Science, vol. 4, no. 1, pp. 79–108, 2011.

S. Dahlke, G. Steidl and G. Teschke, The continuous shearlet transform in arbitrary dimensions, The Journal of Fourier Analysis and ist Applications, vol. 16, no. 3, pp. 340–464, 2010.

S. Didas, G. Steidl and J. Weickert, Integrodiﬀerential equations for multiscale wavelet shrinkage: The discrete case, International Journal of Electrical and Computer Engineering Systems, vol. 1, no. 1, pp. 5–21, 2010.

S. Setzer, G. Steidl and T. Teuber, Deblurring Poissonian images by split Bregman techniques, Journal of Visual Communication and Image Representation, vol. 21, pp. 193–199, 2010.

S. Setzer, G. Steidl, T. Teuber and G. Moerkotte, Approximation related to quotient functionals, Journal of Approximation Theory, vol. 162, no. 3, pp. 545–558, 2010.

G. Steidl and T. Teuber, Removing multiplicative noise by Douglas-Rachford splitting methods, Journal of Mathematical Imaging and Vision, vol. 36, no. 2, pp. 168–184, 2010.

S. Dahlke, G. Kutyniok, G. Steidl and G. Teschke, Shearlet coorbit spaces and associated Banach frames, Applied and Computational Harmonic Analysis, vol. 27, no. 2, pp. 195–214, 2009.

S. Didas, G. Steidl and S. Setzer, Combined l2 data and gradient ﬁtting in conjunction with l1 regularization, Advances in Computational Mathematics, vol. 30, no. 1, pp. 79–99, 2009.

G. Moerkotte, T. Neumann and G. Steidl, Preventing bad plans by bounding the impact of cardinality estimation errors, in Proc. of the VLDB, vol. 2, 2009, pp. 982–993.

S. Setzer, G. Steidl, B. Popilka and B. Burgeth, Variational methods for denoising matrix ﬁelds, in Visualization and Processing of Tensor Fields, Advances and Perspectives, ser. Mathematics and Visualization, D. H. Laidlaw and J. Weickert, Eds., 2009, pp. 341–360.

G. Steidl and T. Teuber, Anisotropic smoothing using double orientation, in Second International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings, A. Lie, M. Lysaker, K. Morken and X.-C. Tai, Eds., Springer, 2009, pp. 477–489.

G. Steidl and T. Teuber, Diﬀusion tensors for denoising sheared and rotated rectangles, IEEE Transactions on Image Processing, vol. 18, no. 12, pp. 2640–2648, 2009.

J. Yuan, C. Schnörr and G. Steidl, Total-variation based piecewise aﬃne regularization, in Second International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings, A. Lie, M. Lysaker, K. Morken and X.-C. Tai, Eds., Springer, 2009, pp. 552–564.

J. Yuan, C. Schnörr and G. Steidl, Convex Hodge decomposition and regularization of image ﬂows, Journal of Mathematical Imaging and Vision, vol. 33, no. 2, pp. 169–177, 2009.

R. H. Chan, S. Setzer and G. Steidl, Inpainting by ﬂexible Haar-wavelet shrinkage, SIAM Journal on Imaging Science, vol. 1, pp. 273–293, 2008.

R. Dahlhaus, J. Franke, J. Polzehl, V. Spokoiny, G. Steidl, J. Weickert, A. Berdychevski, S. Didas, S. Halim, P. Mrazek, S. S. Rao and J. Tadjuidje, Structural adaptive smoothing procedures, in Mathematical Methods for Time Series Analysis and Digital Image Processing, R. Dahlhaus, J. Kurths, P. Maass and J. Timmer, Eds., Berlin: Springer, 2008, pp. 183–229.

S. Dahlke, M. Fornasier, H. Rauhut, G. Steidl and G. Teschke, Generalized coorbit theory, Banach frames and the relation to alpha-modulation spaces, in Proc. London Mathematical Society, vol. 6, 2008, pp. 464–506.

S. Setzer and G. Steidl, Variational methods with higher order derivatives in image processing, in Approximation XII, (San Antonio, USA), M. Neamtu and L. L. Schumaker, Eds., Brentwood: Nashboro Press, 2008, pp. 360–386.

S. Setzer, G. Steidl and T. Teuber, Restoration of images with rotated shapes, Numerical Algorithms, vol. 48, pp. 49–66, 2008.

M. Welk, G. Steidl and J. Weickert, Locally analytic schemes: A link between diﬀusion ﬁltering and wavelet shrinkage, Applied and Computational Harmonic Analysis, vol. 24, pp. 195–224, 2008.

S. Dahlke, G. Steidl and G. Teschke, Frames and coorbit theory on homogeneous spaces with a special guidance on the sphere, The Journal of Fourier Analysis and Applications, vol. 13, no. 4, pp. 387–403, 2007.

B. Popilka, S. Setzer and G. Steidl, Signal recovery from incomplete measurements in the presence of outliers, Inverse Problems and Imaging, vol. 1, no. 4, pp. 661–672, 2007.

G. Steidl, S. Setzer, B. Popilka and B. Burgeth, Restoration of matrix ﬁelds by second order cone programming, Computing, vol. 81, pp. 161–178, 2007.

J. Yuan, C. Schnörr and G. Steidl, Simultaneous higher order optical ﬂow estimation and decomposition, SIAM J. Sci. Comput., vol. 29, no. 6, pp. 2283–2304, 2007.

M. Fenn and G. Steidl, Robust local approximation of scattered data, in Geometric Properties from Incomplete Data, R. Klette, R. Kozera, L. Noakes and J. Weickert, Eds., 2006, pp. 317–334.

S. Kunis, D. Potts and G. Steidl, Fast Gauss transforms with complex parameters using NFFTs, Journal of Numerical Mathematics, vol. 14, no. 4, pp. 295–303, 2006.

G. Plonka and G. Steidl, A multiscale wavelet-inspired scheme for nonlinear diﬀusion, International Journal of Wavelets, Multiresolution and Information Processing, vol. 4, no. 1, pp. 1–21, 2006.

G. Steidl, A note on the dual treatment of higher order regularization functionals, Computing, vol. 76, pp. 135–148, 2006.

G. Steidl, S. Didas and J. Neumann, Splines in higher order TV regularization, International Journal of Computer Vision, vol. 70, pp. 241–255, 2006.

M. Welk, J. Weickert and G. Steidl, From tensor-driven diﬀusion to anisotropic wavelet shrinkage, in Computer Vision - ECCV 2006, H. Bischof, A. Leonardis and A. Pinz, Eds., ser. LNCS, vol. 3951, 2006, pp. 391–403.

A. Kryvanos, J. Hesser and G. Steidl, Nonlinear image restoration methods for marker extraction in 3D ﬂuorescent microscopy, in SPIE’s 17th Annual Symposium EI05 - Electronic Imaging, 2005.

P. Mrazek, J. Weickert and G. Steidl, Diﬀusion-inspired shrinkage functions and stability results for wavelet denoising, International Journal of Computer Vision, vol. 64, no. 2/3, pp. 171–186, 2005.

J. Neumann, C. Schnörr and G. Steidl, Combined SVM-based feature selection and classiﬁcation, Machine Learning, vol. 61, pp. 129–150, 2005.

J. Neumann, C. Schnörr and G. Steidl, Eﬃcient wavelet adaption for hybrid wavelet-large margin classiﬁers, Pattern Recognition, vol. 38, no. 11, pp. 1815–1830, 2005.

J. Neumann and G. Steidl, Dual-tree complex wavelet transform in the frequency domain and an application to signal classiﬁcation, International Journal of Wavelets, Multiresolution and Information Processing, vol. 3, no. 1, pp. 43–66, 2005.

G. Steidl, S. Didas and J. Neumann, Relations between higher order TV regularization and support vector regression, in Scale-Space and PDE Methods in Computer Vision, R. Kimmel, N. Sochen and J. Weickert, Eds., ser. LNCS, vol. 3459, 2005, pp. 515–527.

J. Weickert, G. Steidl, P. Mrazek, M. Welk and T. Brox, Diﬀusion ﬁlters and wavelets: What can they learn from each other? In Handbook of Mathematical Models of Computer Vision, N. Paragios, Y. Chen and O. Faugeras, Eds., 2005, pp. 3–16.

M. Welk, J. Weickert and G. Steidl, A four-pixel scheme for singular diﬀerential equations, in Scale-Space and PDE Methods in Computer Vision, R. Kimmel, N. Sochen and J. Weickert, Eds., 2005, pp. 610–621.

J. Yuan, C. Schnörr, G. Steidl and F. Becker, A study of non-smooth convex ﬂow decomposition, in Proc. Variational, Geometric and Level Set Methods in Computer Vision, ser. LNCS, vol. 3752, 2005, pp. 1–12.

S. Dahlke, G. Steidl and G. Teschke, Coorbit spaces and Banach frames on homogeneous spaces with applications to the sphere, Advances in Computational Mathematics, vol. 21, pp. 147–180, 2004.

S. Dahlke, G. Steidl and G. Teschke, Weighted coorbit spaces and Banach frames on homogeneous spaces, The J. Fourier Anal. Appl., vol. 10, no. 5, pp. 507–539, 2004.

M. Fenn and G. Steidl, Fast NFFT based summation of radial functions, Sampling Theory in Signal and Image Processing, vol. 3, no. 1, pp. 1–28, 2004.

J. Neumann, C. Schnörr and Steidl, SVM-based feature selection by direct objective minimisation, in Pattern Recognition, C. E. Rasmussen, H. H. Bülthoﬀ, M. A. Giese and B. Schölkopf, Eds., ser. LNCS, vol. 3175, 2004, pp. 212–219.

D. Potts, G. Steidl and A. Nieslony, Fast convolution with radial kernels at nonequispaced knots, Numerische Mathematik, vol. 98, no. 2, pp. 329–351, 2004.

G. Steidl, J. Weickert, T. Brox, P. Mrázek and M. Welk, On the equivalence of soft wavelet shrinkage, total variation diﬀusion, total variation regularization, and SIDEs, SIAM Journal on Numerical Analysis, vol. 42, no. 2, pp. 686–713, 2004.

D. J. Strauss, G. Steidl and U. Welzel, Parameter detection of thin ﬁlms from their X-ray reﬂectivity by support vector machines, Applied Numerical Mathematics, vol. 48, pp. 223–236, 2004.

T. Brox, M. Welk, G. Steidl and J. Weickert, Equivalence results for TV diﬀusion and TV regularisation, in Scale-Space Methods in Computer Vision, L. D. Griﬃn and M. Lillholm, Eds., Berlin: Springer, 2003, pp. 86–100.

P. Mrázek, J. Weickert and G. Steidl, Correspondences between wavelet shrinkage and nonlinear diﬀusion, in Scale-Space Methods in Computer Vision, L. D. Griﬃn and M. Lillholm, Eds., Berlin: Springer, 2003, pp. 101–116.

P. Mrázek, J. Weickert, G. Steidl and M. Welk, On iterations and scales of nonlinear ﬁlters, in Proc. Eighth Computer Vision Winter Workshop, O. Drbohlav, Ed., Valtice, Czech Republic: Czech Pattern Recognition Society, 2003, pp. 61–66.

J. Neumann, C. Schnörr and G. Steidl, Feasible adaptation criteria for hybrid wavelet - large margin classiﬁers, in Computer Analysis of Images and Patterns, N. Petkov and M. A. Westenberg, Eds., Berlin: Springer, 2003, pp. 588–599.

A. Nieslony and G. Steidl, Approximate factorizations of Fourier matrices with nonequispaced knots, Linear Algebra and its Applications, vol. 366, pp. 337–351, 2003.

G. Nürnberger, G. Steidl and F. Zeilfelder, Explicit estimates for bivariate hierarchical bases, Communications in Applied Analysis, vol. 7, no. 1, pp. 133–151, 2003.

D. Potts and G. Steidl, Fast summation at nonequispaced knots by NFFTs, SIAM Journal on Scientiﬁc Computing, vol. 24, pp. 2013–2037, 2003.

D. Strauss, G. Steidl and W. Delb, Feature extraction by shape-adapted local discriminant bases, Signal Processing, vol. 83, pp. 359–376, 2003.

M. Fenn and G. Steidl, FMM and H-matrices: a short introduction to the basic idea (teaching material). Preprint Univ. Mannheim, 2002.

D. Potts and G. Steidl, Fourier reconstruction of functions from their nonstandard sampled Radon transform, The Journal of Fourier Analysis and its Applications, vol. 8, pp. 513–533, 2002.

D. Potts and G. Steidl, Rapid evaluation of radial functions by Fast Fast Fourier transform at nonequispaced knots: a users guide to a C-library. Software Guide, Preprint Univ. Lübeck, 2002.

D. Potts, G. Steidl and M. Tasche, Numerical stability of fast trigonometric transforms - a worst case study, Journal of Concrete and Applied Mathematics, vol. 1, pp. 1–36, 2002.

G. Steidl and J. Weickert, Relations between soft wavelet shrinkage and total variation denoising, in Pattern Recognition, L. V. Gool, Ed., ser. LNCS, vol. 2449, 2002, pp. 198–205.

D. Strauss and G. Steidl, Hybrid wavelet-support vector classiﬁcation of waveforms, Journal of Computational and Applied Mathematics, vol. 148, pp. 375–400, 2002.

R. H. Chan, D. Potts and G. Steidl, Preconditioners for non-Hermitian Toeplitz systems, Numerical Linear Algebra and Applications, vol. 8, no. 2, pp. 83–98, 2001.

D. Potts and G. Steidl, A new linogram algorithm for computerized tomography, IMA Journal on Numerical Analysis, vol. 21, pp. 769–782, 2001.

D. Potts and G. Steidl, Preconditioners for ill-conditioned Toeplitz matrices constructed from positive kernels, SIAM Journal on Scientiﬁc Computing, vol. 22, no. 5, pp. 1741–1761, 2001.

D. Potts and G. Steidl, Preconditioning of Hermitian block-Toeplitz-Toeplitz-block matrices by level-1 preconditioners, in Structured Matrices in Mathematics, Computer Science, and Engineering II, V. Olshevsky, Ed., Providence: AMS, 2001, pp. 193–212.

D. Potts, G. Steidl and M. Tasche, Fast Fourier transforms for nonequispaced data: A tutorial, in Modern Sampling Theory: Mathematics and Applications, J. J. Benedetto, P. J. Ferreira and G. Steidl, Eds., Boston: Birkhäuser, 2001, pp. 247–270.

D. Strauss, G. Steidl and J. Jung, Arrhythmia detection using signal-adapted wavelet preprocessing for support vector machines, IEEE Computers in Cardiology, vol. 28, pp. 497–501, 2001.

R. H. Chan, D. Potts and G. Steidl, Preconditioners for nondeﬁnite Hermitian Toeplitz systems, SIAM Journal on Matrix Analysis and Applications, vol. 22, pp. 647–665, 2000.

D. Potts and G. Steidl, New Fourier reconstruction algorithms for computerized tomography, in Wavelet Applications in Signal and Iamge Processing VIII, (San Diego), A. Aldroubi, A. F. Laine and M. A. Unser, Eds., 2000, pp. 13–23.

B. Trebels and G. Steidl, Riesz bounds of Wilson bases generated by B-splines, The Journal of Fourier Analysis and its Applications, vol. 6, no. 2, pp. 159–172, 2000.

A. Gottscheber and G. Steidl, On a family of orthogonal wavelets on the quincunx grid, in Advances in Multivariate Approximation, W. Haussmann, K. Jetter and M. Reimer, Eds., Wiley-VCH, Berlin, 1999, pp. 175–184.

D. Potts and G. Steidl, Preconditioners for ill-conditioned Toeplitz matrices, BIT, vol. 39, no. 3, pp. 513–533, 1999.

A. Elbel and G. Steidl, Fast Fourier transforms for nonequispaced data, in Approximation Theory IX, C. K. Chui and L. L. Schumaker, Eds., Vanderbuilt University Press, 1998, pp. 39–46.

D. Potts and G. Steidl, Optimal trigonometric preconditioners for nonsymmetric Toeplitz systems, Linear Algebra Appl., vol. 281, pp. 265–292, 1998.

D. Potts, G. Steidl and M. Tasche, Fast algorithms for discrete polynomial transforms, Math. Comp., vol. 67, pp. 1577–1590, 1998.

D. Potts, G. Steidl and M. Tasche, Fast and stable algorithms for discrete spherical Fourier transforms, Linear Algebra Appl., vol. 275 - 276, pp. 433–450, 1998.

G. Steidl, A note on fast Fourier transforms for nonequispaced grids, Adv. Comput. Math., vol. 9, pp. 337–353, 1998.

G. Steidl and M. Tasche, Elemente der Fourier-Analysis, in Lehrbriefe Fernuniversität Hagen, 1998.

D. Potts, G. Steidl and M. Tasche, Trigonometric preconditioners for block Toeplitz systems, in Multivariate Approximation and Splines, G. Nürnberger, J. W. Schmidt and G. Walz, Eds., Birkhäuser - Verlag Basel, 1997, pp. 219–234.

D. Potts, G. Steidl and M. Tasche, Kernels of spherical harmonics and spherical frames, in Advanced Topics in Multivariate Approximation, F. Fontanella, K. Jetter and P. J. Laurent, Eds., World Scientiﬁc Publishing Co., Verlag Basel, 1996, pp. 287–301.

B. Glaser, M. Konik and G. Steidl, Multiskalenanalyse des Ankerstroms eines permanentmagneterregten Gleichstrommotors, in Proc. Internat. Conf. on Wavelet-Approximation and Applications, (Lübeck), 1995.

M. Konik, R. Schneider and G. Steidl, Matrix sparsiﬁcation by discrete multiscale methods, in Approximation and Decomposition, C. K. Chui and L. L. Schumaker, Eds., World Scientiﬁc Publishing Co, 1995, pp. 225–234.

G. Steidl, On multivariate attenuation factors, Numer. Algorithms., vol. 9, pp. 245–261, 1995.

G. Steidl, Spline wavelets over R, Z, R/NZ and Z/NZ, in Wavelets: Theory, Algorithms and Applications, C. K. Chui, L. Montefusco and L. Puccio, Eds., Academic Press, 1994, pp. 155–177.

G. Steidl, Chebyshev polynomial derivation of composite-length DCT algorithms, Signal Processing, vol. 29, pp. 17–27, 1992.

G. Steidl, Fast radix-p discrete cosine transform, Appl. Algebra Engrg. Comm. Comput., vol. 3, pp. 39–46, 1992.

G. Steidl and M. Tasche, A polynomial approach to fast algorithms for discrete Fourier-cosine- and Fourier-sine-transforms, Math. Comp., vol. 56, pp. 282–296, 1991.

G. Steidl, M. Tasche and R. Creutzburg, Number-theoretic transforms and a theorem of Sylvester-Kronecker-Zsigmondy, in Computational Number Theory, A. Pethö, M. Pohst, H. C. Williams and H. G. Zimmer, Eds., De Gruyter Berlin - New York, 1991, pp. 45–50.

G. Steidl, Existence and construction of self-complementary normal bases, J. Inf. Process. Cybern., vol. EIK-26, pp. 643–651, 1990.

G. Steidl, Generalization of the algebraic discrete Fourier transform with application to fast convolutions, Linear Algebra Appl., vol. 139, pp. 181–206, 1990.

G. Steidl, On normal bases for ﬁnite commutative rings, Math. Nachr., vol. 145, pp. 131–148, 1990.

G. Steidl and M. Tasche, Fast algorithms for one-and twodimensional discrete cosine transforms, in Multivariate Approximation and Interpolation, W. Haussmann and K. Jetter, Eds., vol. ISNM 94, Birkhäuser - Verlag Basel, 1990, pp. 285–298.

G. Steidl, On symmetric radix-representation of Gaussian integers, BIT, vol. 29, pp. 563–571, 1989.

G. Steidl, M. Hänler and M. Tasche, On a number-theoretic result of Zsigmondy in domains of quadratic integers, Arch. Math., vol. 53, pp. 30–39, 1989.

G. Steidl and M. Tasche, Index transforms for multidimensional DFT’s and convolutions, Numer. Math., vol. 56, pp. 513–528, 1989.

G. Steidl and M. Tasche, Index transforms for multidimensional discrete Fourier transforms, in Multivariate Approximation Theory IV, C. K. Chui, W. Schempp and K. Zeller, Eds., vol. ISNM 90, Birkhäuser - Verlag Basel, 1989, pp. 321–328.

G. Steidl and M. Tasche, On a number-theoretic result of Kronecker-Sylvester-Zsigmondy, Math. Nachr., no. 140, pp. 233–247, 1989.

G. Steidl, Algebraic discrete Fourier transforms and fast convolution algorithms, in Proc. IMYCS’88, Smolenice, 1988, pp. 219–225.

G. Steidl and R. Creutzburg, Number-theoretic transforms in rings of cyclotomic integers, J. Inf. Process. Cybern., vol. EIK-24, pp. 573–584, 1988.

G. Steidl and M. Tasche, Exact deconvolution using number-theoretic transforms, Comput. Math. Appl., vol. 15, pp. 757–768, 1988.

G. Steidl and M. Tasche, Prime factorization for values of cyclotomic polynomials in Z[i], Arch. Math., vol. 49, pp. 292–300, 1987.

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