Stefan Harmeling
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- Advanced Image Processing Techniques 14
- Image and Signal Denoising Methods 10
- Media Technology top 0.2%
- Image Processing Techniques and Applications 8
- Artificial Intelligence top 0.2%
- Neural Networks and Applications 6
- Bayesian Modeling and Causal Inference 5
- Domain Adaptation and Few-Shot Learning 4
- Signal Processing top 2%
- Blind Source Separation Techniques 10
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- Spectroscopy and Chemometric Analyses 4
- Co-authors
- Hannes NickischChristoph H. LampertChristian J. SchulerBernhard SchölkopfH. BurgerMichael HirschJakob ZscheischlerMiguel D. Mahecha
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)Signal Processing (2 papers)Medical Physics (1 paper)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Stefan Harmeling
57 papers receiving 6.2k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Computer Vision and Pattern Recognition 4.2k
- Media Technology 1.4k
- Artificial Intelligence 2.6k
- Signal Processing 273
- Radiology, Nuclear Medicine and Imaging 490
Countries citing papers authored by Stefan Harmeling
This map shows the geographic impact of Stefan Harmeling'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 Stefan Harmeling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Harmeling more than expected).
Fields of papers citing papers by Stefan Harmeling
This network shows the impact of papers produced by Stefan Harmeling. 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 Stefan Harmeling. The network helps show where Stefan Harmeling may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Stefan Harmeling, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 4 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 5 | |
| 8 | 2022 | 13 | |
| 9 | 2022 | 1 | |
| 10 | 2021 | 10 | |
| 11 | 2021 | 9 | |
| 12 | Learning to Deblurbreakdown → | 2015 | 376 |
| 13 | 2014 | 81 | |
| 14 | 2014 | 2 | |
| 15 | 2011 | 30 | |
| 16 | Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake | 2010 | 82 |
| 17 | 2010 | 27 | |
| 18 | Kernel Feature Spaces and Nonlinear Blind Souce Separation | 2001 | 19 |
| 19 | Nonlinear blind source separation using kernel feature spaces | 2001 | 26 |
| 20 | 1998 | 19 |
About Stefan Harmeling
Stefan Harmeling is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 60 papers that have together received 6.4k indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (14 papers), Blind Source Separation Techniques (10 papers), Image and Signal Denoising Methods (10 papers), Image Processing Techniques and Applications (8 papers), Neural Networks and Applications (6 papers), Bayesian Modeling and Causal Inference (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Spectroscopy and Chemometric Analyses (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.2k citations), Media Technology (1.4k citations) and Artificial Intelligence (2.6k citations). Stefan Harmeling has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Hannes Nickisch, Christoph H. Lampert, Christian J. Schuler, Bernhard Schölkopf, H. Burger, Michael Hirsch, Jakob Zscheischler, Miguel D. Mahecha, Heiko H. Schütt and Felix A. Wichmann. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Signal Processing, Medical Physics, Magnetic Resonance in Medicine and NeuroImage.
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.