Peter Maaß

5.1k total citations · 1 hit paper
123 papers, 3.1k citations indexed

About

Peter Maaß is a scholar working on Computer Vision and Pattern Recognition, Mathematical Physics and Biomedical Engineering. According to data from OpenAlex, Peter Maaß has authored 123 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Computer Vision and Pattern Recognition, 29 papers in Mathematical Physics and 22 papers in Biomedical Engineering. Recurrent topics in Peter Maaß's work include Numerical methods in inverse problems (29 papers), Image and Signal Denoising Methods (25 papers) and Mass Spectrometry Techniques and Applications (16 papers). Peter Maaß is often cited by papers focused on Numerical methods in inverse problems (29 papers), Image and Signal Denoising Methods (25 papers) and Mass Spectrometry Techniques and Applications (16 papers). Peter Maaß collaborates with scholars based in Germany, United Kingdom and United States. Peter Maaß's co-authors include Bangti Jin, Carola‐Bibiane Schönlieb, Simon Arridge, Ozan Öktem, Theodore Alexandrov, Andreas Rieder, Alfred K. Louis, Herbert Thiele, Hamid Reza Karimi and Dirk A. Lorenz and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Geophysical Research Atmospheres and Bioinformatics.

In The Last Decade

Peter Maaß

116 papers receiving 3.0k citations

Hit Papers

Solving inverse problems using data-driven models 2019 2026 2021 2023 2019 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Solving inverse problems using data-driven models
    2019 358 citations Simon Arridge, Peter Maaß et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Maaß Germany 32 584 578 569 559 551 123 3.1k
Amit Singer United States 36 629 1.1× 388 0.7× 942 1.7× 323 0.6× 705 1.3× 104 3.8k
Xiaoping Yang China 29 258 0.4× 349 0.6× 112 0.2× 218 0.4× 723 1.3× 262 3.6k
Curtis R. Vogel United States 20 577 1.0× 693 1.2× 111 0.2× 541 1.0× 701 1.3× 38 2.8k
Jérôme Idier France 29 680 1.2× 434 0.8× 140 0.2× 99 0.2× 656 1.2× 144 3.2k
Rafael Molina Spain 47 1.0k 1.8× 738 1.3× 1.2k 2.1× 80 0.1× 3.0k 5.4× 340 8.7k
Lothar Reichel United States 41 1.5k 2.6× 554 1.0× 115 0.2× 2.0k 3.6× 954 1.7× 348 6.9k
M. R. Osborne Australia 26 849 1.5× 218 0.4× 149 0.3× 120 0.2× 370 0.7× 126 3.5k
G. Rodríguez United States 30 258 0.4× 427 0.7× 154 0.3× 454 0.8× 326 0.6× 132 2.6k
Hongkai Zhao United States 31 1.9k 3.3× 944 1.6× 160 0.3× 318 0.6× 579 1.1× 87 4.5k
Kendall Atkinson United States 35 1.4k 2.4× 527 0.9× 123 0.2× 962 1.7× 345 0.6× 81 7.9k

Countries citing papers authored by Peter Maaß

Since Specialization
Citations

This map shows the geographic impact of Peter Maaß'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 Peter Maaß with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Maaß more than expected).

Fields of papers citing papers by Peter Maaß

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Maaß. 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 Peter Maaß. The network helps show where Peter Maaß may publish in the future.

Co-authorship network of co-authors of Peter Maaß

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Maaß. A scholar is included among the top collaborators of Peter Maaß 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 Peter Maaß. Peter Maaß 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.
Chung, Hyungjin, Tae Hoon Roh, Simon Arridge, et al.. (2025). Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction. IEEE Transactions on Medical Imaging. 44(5). 2093–2104.
2.
Fröhlich, Henning, et al.. (2024). Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models. The Journal of the Acoustical Society of America. 156(4). 2448–2466. 1 indexed citations
3.
Jin, Bangti, et al.. (2024). Score-Based Generative Models for PET Image Reconstruction. 2(Generative Models). 547–585. 6 indexed citations
4.
Schlindwein, Vera, et al.. (2024). Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica. Seismological Research Letters. 95(3). 1834–1848. 2 indexed citations
5.
Iben, Uwe, et al.. (2023). Deep learning methods for partial differential equations and related parameter identification problems. Inverse Problems. 39(10). 103001–103001. 28 indexed citations
6.
Maaß, Peter, et al.. (2023). PatchNR: learning from very few images by patch normalizing flow regularization. Inverse Problems. 39(6). 64006–64006. 12 indexed citations
7.
Dittmer, Sören, et al.. (2023). Invertible residual networks in the context of regularization theory for linear inverse problems. Inverse Problems. 39(12). 125018–125018. 4 indexed citations
8.
Jansen, Philipp, Maximilian Schmidt, Jennifer Landsberg, et al.. (2023). Deep learning based histological classification of adnex tumors. European Journal of Cancer. 196. 113431–113431. 3 indexed citations
9.
Schmidt, Maximilian, Klaus Griewank, Eva Hadaschik, et al.. (2023). Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma. JDDG Journal der Deutschen Dermatologischen Gesellschaft. 21(11). 1329–1337. 6 indexed citations
10.
Schmidt, Maximilian, Klaus Griewank, Eva Hadaschik, et al.. (2023). Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms. JDDG Journal der Deutschen Dermatologischen Gesellschaft. 21(11). 1329–1338. 1 indexed citations
11.
Arridge, Simon, Peter Maaß, & Carola‐Bibiane Schönlieb. (2022). Deep Learning for Inverse Problems. Oberwolfach Reports. 18(1). 745–789. 1 indexed citations
12.
Maaß, Peter, et al.. (2022). StainCUT: Stain Normalization with Contrastive Learning. Journal of Imaging. 8(7). 202–202. 11 indexed citations
13.
Schmidt, Maximilian, Poulami Somanya Ganguly, Sophia Bethany Coban, et al.. (2021). Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. Journal of Imaging. 7(3). 44–44. 31 indexed citations
14.
Oetjen, Janina, Andrew Palmer, Theodore Alexandrov, et al.. (2016). An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments. Analytical and Bioanalytical Chemistry. 408(24). 6729–6740. 18 indexed citations
15.
Maaß, Peter, et al.. (2016). An iterative regularization method for nonlinear problems based on Bregman projections. Inverse Problems. 32(11). 115013–115013. 14 indexed citations
16.
Dahlhaus, Rainer, J. Kurths, Peter Maaß, & Jens Timmer. (2008). Mathematical Methods in Signal Processing and Digital Image Analysis (Understanding Complex Systems). Springer eBooks. 5 indexed citations
17.
Huffel, Sabine Van, et al.. (2004). Special issue on linear algebra in signal and image processing - Preface. Linear Algebra and its Applications. 391. 1–1. 5 indexed citations
18.
Ricke, Jens, Peter Maaß, E Lopez Hänninen, et al.. (1998). Wavelet Versus JPEG (Joint Photographic Expert Group) and Fractal Compression. Investigative Radiology. 33(8). 456–463. 24 indexed citations
19.
Louis, Alfred K., Peter Maaß, Andreas Rieder, & Hans‐Georg Stark. (1994). Wavelets and digital image processing. publish.UP (University of Potsdam). 4 indexed citations
20.
Louis, Alfred K., Peter Maaß, & Andreas Rieder. (1994). Wavelets : theorie und anwendungen. publish.UP (University of Potsdam). 25 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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