Karen Panetta
- Computer Vision and Pattern Recognition top 0.1%
- Media Technology top 0.1%
- Artificial Intelligence top 5%
- Atomic and Molecular Physics, and Optics top 10%
- Biomedical Engineering top 10%
- Co-authors
- Sos С. AgaianChen GaoYicong ZhouE. J. WhartonArtyom M. GrigoryanC. L. Philip ChenRahul RajendranShishir Paramathma Rao
- Topics
- Image Enhancement Techniques (69 papers)Image and Signal Denoising Methods (50 papers)Advanced Image Fusion Techniques (35 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- United StatesMacaoFrance
In The Last Decade
Karen Panetta
184 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 150
- Computer Vision and Pattern Recognition 4.0k
- Media Technology 1.6k
- Artificial Intelligence 387
- Atomic and Molecular Physics, and Optics 310
- Biomedical Engineering 273
Countries citing papers authored by Karen Panetta
This map shows the geographic impact of Karen Panetta'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 Karen Panetta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karen Panetta more than expected).
Fields of papers citing papers by Karen Panetta
This network shows the impact of papers produced by Karen Panetta. 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 Karen Panetta. The network helps show where Karen Panetta may publish in the future.
Co-authorship network of co-authors of Karen Panetta
This figure shows the co-authorship network connecting the top 25 collaborators of Karen Panetta. A scholar is included among the top collaborators of Karen Panetta 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 Karen Panetta. Karen Panetta 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 66 | |
| 6 | 9 | |
| 7 | 6 | |
| 8 | 114 | |
| 9 | 27 | |
| 10 | 42 | |
| 11 | 8 | |
| 12 | 152 | |
| 13 | 29 | |
| 14 | 14 | |
| 15 | 196 | |
| 16 | 3 | |
| 17 | 64 | |
| 18 | Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropybreakdown → | 423 |
| 19 | Transform-based image compression by noise reduction and spatial modification using Boolean minimization | 4 |
| 20 | 303 |
About Karen Panetta
Karen Panetta is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 194 papers that have together received 4.7k indexed citations. Recurring topics across this work include Image Enhancement Techniques (69 papers), Image and Signal Denoising Methods (50 papers) and Advanced Image Fusion Techniques (35 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.0k citations), Media Technology (1.6k citations) and Signal Processing (210 citations). Karen Panetta has collaborated with scholars based in United States, Macao and France. Frequent co-authors include Sos С. Agaian, Chen Gao, Yicong Zhou, E. J. Wharton, Artyom M. Grigoryan, C. L. Philip Chen, Rahul Rajendran, Shishir Paramathma Rao, Hongwei Jia and Long Bao. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.