Gabriele Steidl
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 5%
- Computational Mechanics top 10%
- Applied Mathematics top 5%
- Signal Processing top 10%
- Co-authors
- Daniel PottsManfred TascheDaniel J. StraußGerd TeschkeStephan DahlkeJoachim WeickertMila NikolovaTanja Teuber
- Topics
- Sparse and Compressive Sensing Techniques (9 papers)Image and Signal Denoising Methods (7 papers)Matrix Theory and Algorithms (5 papers)
- Journals
- IEEE Transactions on Image ProcessingMathematics of ComputationInternational Journal of Computer Vision
- Partner nations
- GermanyFranceUnited States
In The Last Decade
Gabriele Steidl
37 papers receiving 419 citations
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 174
- Computational Theory and Mathematics 116
- Computational Mechanics 111
- Applied Mathematics 100
- Signal Processing 77
Countries citing papers authored by Gabriele Steidl
This map shows the geographic impact of Gabriele Steidl'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 Gabriele Steidl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Steidl more than expected).
Fields of papers citing papers by Gabriele Steidl
This network shows the impact of papers produced by Gabriele Steidl. 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 Gabriele Steidl. The network helps show where Gabriele Steidl may publish in the future.
Co-authorship network of co-authors of Gabriele Steidl
This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Steidl. A scholar is included among the top collaborators of Gabriele Steidl 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 Gabriele Steidl. Gabriele Steidl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 8 | |
| 8 | 1 | |
| 9 | 23 | |
| 10 | 23 | |
| 11 | 29 | |
| 12 | 19 | |
| 13 | 60 | |
| 14 | 32 | |
| 15 | 15 | |
| 16 | 1 | |
| 17 | 1 | |
| 18 | 0 | |
| 19 | 5 | |
| 20 | 2 |
About Gabriele Steidl
Gabriele Steidl is a scholar working on Computational Mathematics, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 41 papers that have together received 455 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (9 papers), Image and Signal Denoising Methods (7 papers) and Matrix Theory and Algorithms (5 papers). The work is most often cited by research in Computational Mathematics (11 citations), Applied Mathematics (100 citations) and Computer Vision and Pattern Recognition (174 citations). Gabriele Steidl has collaborated with scholars based in Germany, France and United States. Frequent co-authors include Daniel Potts, Manfred Tasche, Daniel J. Strauß, Gerd Teschke, Stephan Dahlke, Joachim Weickert, Mila Nikolova, Tanja Teuber, Pavel Mrázek⋆ and W. Delb. Their work appears in journals such as IEEE Transactions on Image Processing, Mathematics of Computation and International Journal of Computer Vision.
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.