World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
44
Citations
7729
World Ranking
1591
National Ranking
94

Gabriele Steidl publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Gabriele Steidl sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 250 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Gabriele Steidl D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Gabriele Steidl sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 44 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Gabriele Steidl is affiliated with the Technical University of Berlin in Germany. Their research spans several fields, primarily focusing on Mathematics, Computer Science, and Engineering. Within these domains, their work extensively covers subfields such as Applied Mathematics, Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, and Mathematical Physics.

The scientist has contributed to multiple main topics, including:

  • Image and Signal Denoising Methods
  • Sparse and Compressive Sensing Techniques
  • Numerical methods in inverse problems
  • Geometric Analysis and Curvature Flows
  • Medical Imaging Techniques and Applications
  • Seismic Imaging and Inversion Techniques
  • Advanced Numerical Analysis Techniques

Recent papers by Gabriele Steidl include the following publications:

  • "Parseval Proximal Neural Networks," 2020, Journal of Fourier Analysis and Applications
  • "Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint," 2022, SIAM/ASA Journal on Uncertainty Quantification
  • "PatchNR: learning from very few images by patch normalizing flow regularization," 2023, Inverse Problems
  • "Unbalanced Multi-marginal Optimal Transport," 2022, Journal of Mathematical Imaging and Vision
  • "Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization," 2021, Foundations of Computational Mathematics

Frequent collaborators in Steidl's work include Johannes Hertrich, Robert Beinert, Gerlind Plonka, Paul Hagemann, and Michael Quellmalz.

Publication venues where Gabriele Steidl has frequently contributed are:

  • arXiv (Cornell University)
  • Inverse Problems
  • Journal of Mathematical Imaging and Vision
  • Sampling Theory Signal Processing and Data Analysis
  • Journal of Fourier Analysis and Applications

Their academic output also includes book publications with established publishers. Two noted titles are:

  • "Numerical Fourier Analysis" (2023) published by Springer International Publishing
  • "Generalized Normalizing Flows via Markov Chains" (2023) published by Cambridge University Press

Best Publications

  • Deblurring Poissonian images by split Bregman techniques

    S. Setzer;G. Steidl;T. Teuber

  • Fast Fourier transforms for nonequispaced data: a tutorial

    Daniel Potts;Gabriele Steidl;Manfred Tasche

  • Combined SVM-Based Feature Selection and Classification

    Julia Neumann;Christoph Schnörr;Gabriele Steidl

  • On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs

    Gabriele Steidl;Joachim Weickert;Thomas Brox;Pavel Mrázek

  • Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

    G. Steidl;T. Teuber

  • Preventing bad plans by bounding the impact of cardinality estimation errors

    Guido Moerkotte;Thomas Neumann;Gabriele Steidl

  • A note on fast Fourier transforms for nonequispaced grids

    Gabriele Steidl

  • Shearlet coorbit spaces and associated Banach frames

    Stephan Dahlke;Gitta Kutyniok;Gabriele Steidl;Gerd Teschke

  • Fast Summation at Nonequispaced Knots by NFFT

    Daniel Potts;Gabriele Steidl

  • Numerical Fourier Analysis

    Gerlind Plonka;Daniel Potts;Gabriele Steidl;Manfred Tasche

  • The Continuous Shearlet Transform in Arbitrary Space Dimensions

    Stephan Dahlke;Gabriele Steidl;Gerd Teschke

  • Fast algorithms for discrete polynomial transforms

    Daniel Potts;Gabriele Steidl;Manfred Tasche

  • Infimal convolution regularizations with discrete ℓ1-type functionals

    S. Setzer;G. Steidl;T. Teuber

  • Shearlet Coorbit Spaces: Compactly Supported Analyzing Shearlets, Traces and Embeddings

    Stephan Dahlke;Gabriele Steidl;Gerd Teschke

  • Fast Hue and Range Preserving Histogram: Specification: Theory and New Algorithms for Color Image Enhancement.

    Mila Nikolova;Gabriele Steidl

  • First Order Algorithms in Variational Image Processing

    Martin Burger;Alexander Sawatzky;Gabriele Steidl

  • Fast convolution with radial kernels at nonequispaced knots

    Daniel Potts;Gabriele Steidl;Arthur Nieslony

  • A Note on the Dual Treatment of Higher-Order Regularization Functionals

    G. Steidl

  • Splines in Higher Order TV Regularization

    Gabriele Steidl;Stephan Didas;Julia Neumann

  • A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

    Yanyan He;M. Yousuff Hussaini;Jianwei Ma;Behrang Shafei

Frequent Co-Authors

Daniel Potts
Daniel Potts Chemnitz University of Technology
Stephan Dahlke
Stephan Dahlke Philipp University of Marburg
Joachim Weickert
Joachim Weickert Saarland University
Christoph Schnörr
Christoph Schnörr Heidelberg University
Mila Nikolova
Mila Nikolova École Normale Supérieure Paris-Saclay
Raymond H. Chan
Raymond H. Chan Lingnan University
Gitta Kutyniok
Gitta Kutyniok Ludwig-Maximilians-Universität München
Jean-François Aujol
Jean-François Aujol University of Bordeaux
Guido Moerkotte
Guido Moerkotte University of Mannheim
Jalal M. Fadili
Jalal M. Fadili École Nationale Supérieure d'Ingénieurs de Caen

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