Markus Grasmair

1.6k total citations
28 papers, 901 citations indexed

About

Markus Grasmair is a scholar working on Mathematical Physics, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Markus Grasmair has authored 28 papers receiving a total of 901 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Mathematical Physics, 13 papers in Computational Mechanics and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Markus Grasmair's work include Numerical methods in inverse problems (14 papers), Sparse and Compressive Sensing Techniques (12 papers) and Image and Signal Denoising Methods (4 papers). Markus Grasmair is often cited by papers focused on Numerical methods in inverse problems (14 papers), Sparse and Compressive Sensing Techniques (12 papers) and Image and Signal Denoising Methods (4 papers). Markus Grasmair collaborates with scholars based in Austria, Norway and Germany. Markus Grasmair's co-authors include Otmar Scherzer, Markus Haltmeier, Frank Lenzen, Harald Grossauer, Ferdinand von Eggeling, Michael Becker, Sören‐Oliver Deininger, Peter Maaß, Theodore Alexandrov and Herbert Thiele and has published in prestigious journals such as PLoS ONE, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Markus Grasmair

23 papers receiving 839 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Markus Grasmair Austria 11 428 426 232 231 128 28 901
Xiliang Lu China 16 332 0.8× 152 0.4× 93 0.4× 265 1.1× 27 0.2× 67 830
Christian Clason Austria 19 406 0.9× 351 0.8× 167 0.7× 114 0.5× 241 1.9× 52 974
Frank Filbir Germany 14 75 0.2× 82 0.2× 50 0.2× 65 0.3× 63 0.5× 40 444
Elena Loli Piccolomini Italy 14 171 0.4× 68 0.2× 133 0.6× 226 1.0× 130 1.0× 67 615
Andreas Weinmann Germany 12 170 0.4× 40 0.1× 137 0.6× 209 0.9× 53 0.4× 55 513
Anders C. Hansen United Kingdom 17 405 0.9× 143 0.3× 266 1.1× 268 1.2× 481 3.8× 39 1.2k
Matthias J. Ehrhardt United Kingdom 14 184 0.4× 48 0.1× 173 0.7× 132 0.6× 429 3.4× 46 719
Francine Catté France 4 224 0.5× 142 0.3× 61 0.3× 848 3.7× 113 0.9× 7 1.1k
Mårten Gulliksson Sweden 14 99 0.2× 122 0.3× 33 0.1× 41 0.2× 25 0.2× 55 486
Dennis M. Healy United States 12 336 0.8× 21 0.0× 290 1.3× 213 0.9× 172 1.3× 46 787

Countries citing papers authored by Markus Grasmair

Since Specialization
Citations

This map shows the geographic impact of Markus Grasmair's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markus Grasmair with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Grasmair more than expected).

Fields of papers citing papers by Markus Grasmair

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Grasmair. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markus Grasmair. The network helps show where Markus Grasmair may publish in the future.

Co-authorship network of co-authors of Markus Grasmair

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Grasmair. A scholar is included among the top collaborators of Markus Grasmair based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Markus Grasmair. Markus Grasmair is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Grasmair, Markus, et al.. (2025). Subgradient-based Lavrentiev regularisation of monotone ill-posed problems. Inverse Problems. 41(4). 45013–45013.
2.
3.
Nygård, Jan F., et al.. (2022). Matrix factorization for the reconstruction of cervical cancer screening histories and prediction of future screening results. BMC Bioinformatics. 23(S12). 484–484. 2 indexed citations
4.
Nygård, Jan F., et al.. (2022). Towards a data-driven system for personalized cervical cancer risk stratification. Scientific Reports. 12(1). 12083–12083. 8 indexed citations
5.
Gogineni, Vinay Chakravarthi, et al.. (2021). Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective. 46–50. 6 indexed citations
6.
Grasmair, Markus. (2020). Source Conditions for Non-Quadratic Tikhonov Regularization. Numerical Functional Analysis and Optimization. 41(11). 1352–1372. 2 indexed citations
7.
Grasmair, Markus. (2013). Variational inequalities and higher order convergence rates for Tikhonov regularisation on Banach spaces. Journal of Inverse and Ill-Posed Problems. 21(3). 379–394. 10 indexed citations
8.
Grasmair, Markus, Otmar Scherzer, & Anne Vanhems. (2013). Nonparametric instrumental regression with non-convex constraints. Inverse Problems. 29(3). 35006–35006. 4 indexed citations
9.
Grasmair, Markus, et al.. (2012). Shape Reconstruction with A Priori Knowledge Based on Integral Invariants. SIAM Journal on Imaging Sciences. 5(2). 726–745. 5 indexed citations
10.
Grasmair, Markus, Markus Haltmeier, & Otmar Scherzer. (2011). The residual method for regularizing ill-posed problems. Applied Mathematics and Computation. 218(6). 2693–2710. 20 indexed citations
11.
Alexandrov, Theodore, Michael Becker, Sören‐Oliver Deininger, et al.. (2010). Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering. Journal of Proteome Research. 9(12). 6535–6546. 157 indexed citations
12.
Elbau, Peter, Markus Grasmair, Frank Lenzen, & Otmar Scherzer. (2010). Evolution by Non-Convex Functionals. Numerical Functional Analysis and Optimization. 31(4). 489–517. 1 indexed citations
13.
Grasmair, Markus, Otmar Scherzer, & Markus Haltmeier. (2010). Necessary and sufficient conditions for linear convergence of ℓ1-regularization. Communications on Pure and Applied Mathematics. 64(2). 161–182. 62 indexed citations
14.
Grasmair, Markus & Frank Lenzen. (2010). Anisotropic Total Variation Filtering. Applied Mathematics & Optimization. 62(3). 323–339. 75 indexed citations
15.
Grasmair, Markus. (2009). Non-convex sparse regularisation. Journal of Mathematical Analysis and Applications. 365(1). 19–28. 23 indexed citations
16.
Grasmair, Markus, et al.. (2008). Generalizations of the Taut String Method. Numerical Functional Analysis and Optimization. 29(3-4). 346–361. 3 indexed citations
17.
Grasmair, Markus, Markus Haltmeier, & Otmar Scherzer. (2008). Sparse regularization with l q penalty term. Inverse Problems. 24(5). 55020–55020. 109 indexed citations
18.
Scherzer, Otmar, Frank Lenzen, Markus Haltmeier, Harald Grossauer, & Markus Grasmair. (2008). Variational Methods in Imaging. Applied mathematical sciences. 277 indexed citations
19.
Grasmair, Markus. (2006). Relaxation of Nonlocal Integrals with Rational Integrands. 2 indexed citations
20.
Grasmair, Markus & Otmar Scherzer. (2005). Relaxation of Nonlocal Singular Integrals. Numerical Functional Analysis and Optimization. 26(4-5). 481–506. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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