Miguel Vega
- Computer Vision and Pattern Recognition top 2%
- Media Technology top 0.5%
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Biomedical Engineering
- Co-authors
- Rafael MolinaAggelos K. KatsaggelosJavier MateosS. Derin BabacanH.G. SarmientoValery NaranjoXu ZhouFugen Zhou
- Topics
- Advanced Image Processing Techniques (33 papers)Image and Signal Denoising Methods (30 papers)Advanced Image Fusion Techniques (16 papers)
- Partner nations
- SpainUnited StatesMexico
In The Last Decade
Miguel Vega
54 papers receiving 773 citations
Peers
Comparison fields: 5 of 73
- Computer Vision and Pattern Recognition 572
- Media Technology 477
- Artificial Intelligence 107
- Computational Mechanics 72
- Biomedical Engineering 57
Countries citing papers authored by Miguel Vega
This map shows the geographic impact of Miguel Vega'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 Miguel Vega with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miguel Vega more than expected).
Fields of papers citing papers by Miguel Vega
This network shows the impact of papers produced by Miguel Vega. 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 Miguel Vega. The network helps show where Miguel Vega may publish in the future.
Co-authorship network of co-authors of Miguel Vega
This figure shows the co-authorship network connecting the top 25 collaborators of Miguel Vega. A scholar is included among the top collaborators of Miguel Vega 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 Miguel Vega. Miguel Vega 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 | 13 | |
| 3 | 25 | |
| 4 | 16 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 13 | |
| 8 | 5 | |
| 9 | A general sparse image prior combination in Compressed Sensing | 2 |
| 10 | 9 | |
| 11 | 6 | |
| 12 | 6 | |
| 13 | 12 | |
| 14 | 20 | |
| 15 | 47 | |
| 16 | Hierarchical Bayesian super resolution reconstruction of multispectral images | 4 |
| 17 | 10 | |
| 18 | 43 | |
| 19 | 52 | |
| 20 | 4 |
About Miguel Vega
Miguel Vega is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Biophysics, having authored 55 papers that have together received 808 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (33 papers), Image and Signal Denoising Methods (30 papers) and Advanced Image Fusion Techniques (16 papers). The work is most often cited by research in Media Technology (477 citations), Computer Vision and Pattern Recognition (572 citations) and Biophysics (39 citations). Miguel Vega has collaborated with scholars based in Spain, United States and Mexico. Frequent co-authors include Rafael Molina, Aggelos K. Katsaggelos, Javier Mateos, S. Derin Babacan, H.G. Sarmiento, Valery Naranjo, Xu Zhou, Fugen Zhou, José Aneiros‐Fernández and Esteban Vera. Their work appears in journals such as IEEE Transactions on Image Processing, Sensors and IEEE Transactions on Industry Applications.
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.