Saïd Ladjal
- Computer Vision and Pattern Recognition top 5%
- Media Technology top 2%
- Aerospace Engineering
- Computational Mechanics top 10%
- Artificial Intelligence
- Co-authors
- Marouan BoualiJean–François AujolSimon MasnouYann GousseauBin LuoAlasdair NewsonSylvain BerlemontChi-Hieu Pham
- Topics
- Image and Signal Denoising Methods (9 papers)Sparse and Compressive Sensing Techniques (6 papers)Generative Adversarial Networks and Image Synthesis (6 papers)
- Cited by
- Media TechnologyComputer Vision and Pattern RecognitionComputer Graphics and Computer-Aided Design
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Geoscience and Remote SensingIEEE Transactions on Image Processing
In The Last Decade
Saïd Ladjal
22 papers receiving 414 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 283
- Media Technology 182
- Aerospace Engineering 70
- Computational Mechanics 65
- Artificial Intelligence 54
Countries citing papers authored by Saïd Ladjal
This map shows the geographic impact of Saïd Ladjal'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 Saïd Ladjal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saïd Ladjal more than expected).
Fields of papers citing papers by Saïd Ladjal
This network shows the impact of papers produced by Saïd Ladjal. 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 Saïd Ladjal. The network helps show where Saïd Ladjal may publish in the future.
Co-authorship network of co-authors of Saïd Ladjal
This figure shows the co-authorship network connecting the top 25 collaborators of Saïd Ladjal. A scholar is included among the top collaborators of Saïd Ladjal 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 Saïd Ladjal. Saïd Ladjal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction | 2 |
| 2 | 2 | |
| 3 | 17 | |
| 4 | 4 | |
| 5 | 26 | |
| 6 | 13 | |
| 7 | 5 | |
| 8 | 18 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 6 | |
| 12 | 60 | |
| 13 | 5 | |
| 14 | 28 | |
| 15 | 16 | |
| 16 | 1 | |
| 17 | Superresolution aveugle d'images par la methode des sous-espaces | 0 |
| 18 | Resolution independent characteristic scale with application to satellite images Echelle caractéristique indépendante de la résolution et application aux images satellitaires | 1 |
| 19 | 4 | |
| 20 | 17 |
About Saïd Ladjal
Saïd Ladjal is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Biophysics, having authored 24 papers that have together received 431 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (9 papers), Sparse and Compressive Sensing Techniques (6 papers) and Generative Adversarial Networks and Image Synthesis (6 papers). The work is most often cited by research in Media Technology (182 citations), Computer Vision and Pattern Recognition (283 citations) and Computer Graphics and Computer-Aided Design (19 citations). Saïd Ladjal has collaborated with scholars based in France and Japan. Frequent co-authors include Marouan Bouali, Jean–François Aujol, Simon Masnou, Yann Gousseau, Bin Luo, Alasdair Newson, Sylvain Berlemont, Chi-Hieu Pham, Isabelle Bloch and Florence Tupin. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing 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.