Andreas Lugmayr
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 10%
- Computer Graphics and Computer-Aided Design top 2%
- Media Technology top 5%
- Computational Mechanics top 10%
- Co-authors
- Radu TimofteMartin DanelljanLuc Van GoolFisher YuAndrés RomeroKai ZhangJingyun LiangJulien Lamour
- Topics
- Advanced Image Processing Techniques (5 papers)Advanced Vision and Imaging (4 papers)Image and Signal Denoising Methods (2 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionMedia Technology
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Partner nations
- SwitzerlandUnited StatesBelgium
In The Last Decade
Andreas Lugmayr
6 papers receiving 901 citations
Hit Papers
Peers
Comparison fields: 5 of 93
- Computer Vision and Pattern Recognition 671
- Artificial Intelligence 137
- Computer Graphics and Computer-Aided Design 117
- Media Technology 108
- Computational Mechanics 95
Countries citing papers authored by Andreas Lugmayr
This map shows the geographic impact of Andreas Lugmayr'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 Andreas Lugmayr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Lugmayr more than expected).
Fields of papers citing papers by Andreas Lugmayr
This network shows the impact of papers produced by Andreas Lugmayr. 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 Andreas Lugmayr. The network helps show where Andreas Lugmayr may publish in the future.
Co-authorship network of co-authors of Andreas Lugmayr
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Lugmayr. A scholar is included among the top collaborators of Andreas Lugmayr 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 Andreas Lugmayr. Andreas Lugmayr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | RePaint: Inpainting using Denoising Diffusion Probabilistic Modelsbreakdown → | 797 |
| 3 | 7 | |
| 4 | 10 | |
| 5 | 73 | |
| 6 | 43 |
About Andreas Lugmayr
Andreas Lugmayr is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Media Technology, having authored 6 papers that have together received 932 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (4 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (117 citations), Computer Vision and Pattern Recognition (671 citations) and Media Technology (108 citations). Andreas Lugmayr has collaborated with scholars based in Switzerland, United States and Belgium. Frequent co-authors include Radu Timofte, Martin Danelljan, Luc Van Gool, Fisher Yu, Andrés Romero, Kai Zhang, Jingyun Liang, Julien Lamour, Shuhang Gu and Cong Wang. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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.