Yaniv Romano
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- Image and Signal Denoising Methods 13
- Advanced Image Processing Techniques 8
- Generative Adversarial Networks and Image Synthesis 2
- Media Technology top 1%
- Advanced Image Fusion Techniques 4
- Image Processing Techniques and Applications 3
- Computational Mechanics top 5%
- Sparse and Compressive Sensing Techniques 9
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- Neural Networks and Applications 2
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- Seismic Imaging and Inversion Techniques 2
- Co-authors
- Michael EladPeyman MilanfarJohn IsidoroJeremias SulamVardan PapyanMatan ProtterEmmanuel J. CandèsMatteo Sesia
- Journals
- SHILAP Revista de lepidopterología (2 papers)Journal of the American Statistical Association (1 paper)IEEE Transactions on Image Processing (3 papers)
- Partner nations
- IsraelUnited States
In The Last Decade
Yaniv Romano
20 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 88
- Computer Vision and Pattern Recognition 818
- Media Technology 343
- Computational Mechanics 336
- Acoustics and Ultrasonics 11
- Computational Mathematics 6
Countries citing papers authored by Yaniv Romano
This map shows the geographic impact of Yaniv Romano'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 Yaniv Romano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yaniv Romano more than expected).
Fields of papers citing papers by Yaniv Romano
This network shows the impact of papers produced by Yaniv Romano. 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 Yaniv Romano. The network helps show where Yaniv Romano may publish in the future.
Co-authorship network
The 16 scholars most cited alongside Yaniv Romano, 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 | 2024 | 0 | |
| 2 | 2022 | 0 | |
| 3 | Achieving Equalized Odds by Resampling Sensitive Attributes | 2020 | 1 |
| 4 | 2020 | 17 | |
| 5 | 2019 | 60 | |
| 6 | 2019 | 11 | |
| 7 | 2019 | 28 | |
| 8 | 2019 | 2 | |
| 9 | 2018 | 76 | |
| 10 | 2018 | 3 | |
| 11 | 2018 | 63 | |
| 12 | The Little Engine That Could: Regularization by Denoising (RED)breakdown → | 2017 | 476 |
| 13 | 2017 | 6 | |
| 14 | 2016 | 52 | |
| 15 | 2016 | 3 | |
| 16 | 2016 | 188 | |
| 17 | 2015 | 15 | |
| 18 | 2015 | 101 | |
| 19 | 2014 | 89 | |
| 20 | 2013 | 24 |
About Yaniv Romano
Yaniv Romano is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 23 papers that have together received 1.2k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (13 papers), Sparse and Compressive Sensing Techniques (9 papers), Advanced Image Processing Techniques (8 papers), Advanced Image Fusion Techniques (4 papers), Image Processing Techniques and Applications (3 papers), Neural Networks and Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Seismic Imaging and Inversion Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (818 citations), Media Technology (343 citations) and Computational Mechanics (336 citations). Yaniv Romano has collaborated with scholars based in Israel and United States. Frequent co-authors include Michael Elad, Peyman Milanfar, John Isidoro, Jeremias Sulam, Vardan Papyan, Matan Protter, Emmanuel J. Candès, Matteo Sesia, Ronen Talmon and Tao Hong. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and IEEE Transactions on Image Processing.
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.