Pasquale De Michele
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Computer Networks and Communications top 10%
- Computational Mechanics
- Media Technology top 10%
- Co-authors
- Salvatore CuomoFrancesco PiccialliArdelio GallettiJai E. JungLivia MarcellinoGiovanni PontiGiuseppe PalmaPasquale Borrelli
- Topics
- Image and Signal Denoising Methods (7 papers)Sparse and Compressive Sensing Techniques (6 papers)Neural Networks and Applications (5 papers)
- Partner nations
- ItalySouth KoreaIndia
In The Last Decade
Pasquale De Michele
30 papers receiving 376 citations
Peers
Comparison fields: 5 of 95
- Computer Vision and Pattern Recognition 149
- Artificial Intelligence 83
- Computer Networks and Communications 63
- Computational Mechanics 53
- Media Technology 39
Countries citing papers authored by Pasquale De Michele
This map shows the geographic impact of Pasquale De Michele'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 Pasquale De Michele with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pasquale De Michele more than expected).
Fields of papers citing papers by Pasquale De Michele
This network shows the impact of papers produced by Pasquale De Michele. 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 Pasquale De Michele. The network helps show where Pasquale De Michele may publish in the future.
Co-authorship network of co-authors of Pasquale De Michele
This figure shows the co-authorship network connecting the top 25 collaborators of Pasquale De Michele. A scholar is included among the top collaborators of Pasquale De Michele 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 Pasquale De Michele. Pasquale De Michele is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 73 | |
| 5 | 6 | |
| 6 | 10 | |
| 7 | 14 | |
| 8 | 11 | |
| 9 | 3 | |
| 10 | 7 | |
| 11 | 3 | |
| 12 | 11 | |
| 13 | 2 | |
| 14 | 4 | |
| 15 | 3 | |
| 16 | 3D Non-Local Means denoising via multi-GPU | 30 |
| 17 | 4 | |
| 18 | 38 | |
| 19 | 22 | |
| 20 | 2 |
About Pasquale De Michele
Pasquale De Michele is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 30 papers that have together received 385 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (149 citations), Media Technology (39 citations) and Statistical and Nonlinear Physics (36 citations). Pasquale De Michele has collaborated with scholars based in Italy, South Korea and India. Frequent co-authors include Salvatore Cuomo, Francesco Piccialli, Ardelio Galletti, Jai E. Jung, Livia Marcellino, Giovanni Ponti, Giuseppe Palma, Pasquale Borrelli, Bruno Alfano and Hélène Marie. Their work appears in journals such as Expert Systems with Applications, Hippocampus and Journal of Parallel and Distributed Computing.
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.