Javier Mateos

2.2k total citations
80 papers, 1.5k citations indexed

About

Javier Mateos is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Javier Mateos has authored 80 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Computer Vision and Pattern Recognition, 42 papers in Media Technology and 11 papers in Artificial Intelligence. Recurrent topics in Javier Mateos's work include Advanced Image Processing Techniques (53 papers), Image and Signal Denoising Methods (51 papers) and Advanced Image Fusion Techniques (23 papers). Javier Mateos is often cited by papers focused on Advanced Image Processing Techniques (53 papers), Image and Signal Denoising Methods (51 papers) and Advanced Image Fusion Techniques (23 papers). Javier Mateos collaborates with scholars based in Spain, United States and Canada. Javier Mateos's co-authors include Rafael Molina, Aggelos K. Katsaggelos, Miguel Vega, C.A. Segall, J. Núñez, Pablo Ruiz, Xu Zhou, Gustau Camps‐Valls, Fugen Zhou and A. Gil de Paz and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and Sensors.

In The Last Decade

Javier Mateos

76 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Javier Mateos Spain 18 1.1k 766 194 135 88 80 1.5k
Moon Gi Kang South Korea 3 2.0k 1.7× 1.1k 1.5× 208 1.1× 41 0.3× 155 1.8× 7 2.3k
Moon Gi Kang South Korea 19 1.0k 0.9× 602 0.8× 98 0.5× 88 0.7× 123 1.4× 127 1.4k
Catalina Sbert Spain 18 1.2k 1.1× 561 0.7× 184 0.9× 40 0.3× 96 1.1× 39 1.5k
Ting Lu China 20 635 0.6× 1.4k 1.8× 372 1.9× 175 1.3× 117 1.3× 97 2.2k
Charles‐Alban Deledalle France 17 1.6k 1.4× 1.1k 1.4× 410 2.1× 92 0.7× 156 1.8× 49 2.5k
Luís Garrido Spain 10 719 0.6× 223 0.3× 42 0.2× 101 0.7× 41 0.5× 28 1.2k
Tsung‐Han Chan Taiwan 19 569 0.5× 1.2k 1.5× 359 1.9× 318 2.4× 103 1.2× 50 1.8k
M.R. Banham United States 8 1.1k 0.9× 556 0.7× 260 1.3× 53 0.4× 136 1.5× 15 1.3k
Yann Gousseau France 22 1.4k 1.2× 364 0.5× 151 0.8× 87 0.6× 33 0.4× 60 1.8k
Jean–François Aujol France 26 1.9k 1.6× 629 0.8× 736 3.8× 116 0.9× 178 2.0× 81 2.4k

Countries citing papers authored by Javier Mateos

Since Specialization
Citations

This map shows the geographic impact of Javier Mateos'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 Javier Mateos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Mateos more than expected).

Fields of papers citing papers by Javier Mateos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Javier Mateos. 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 Javier Mateos. The network helps show where Javier Mateos may publish in the future.

Co-authorship network of co-authors of Javier Mateos

This figure shows the co-authorship network connecting the top 25 collaborators of Javier Mateos. A scholar is included among the top collaborators of Javier Mateos 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 Javier Mateos. Javier Mateos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Amor, Rocío del, Sandra Morales, José Aneiros‐Fernández, et al.. (2025). A fusocelular skin dataset with whole slide images for deep learning models. Scientific Data. 12(1). 788–788.
2.
Mateos, Javier, et al.. (2024). The CrowdGleason dataset: Learning the Gleason grade from crowds and experts. Computer Methods and Programs in Biomedicine. 257. 108472–108472.
3.
4.
Amor, Rocío del, José Aneiros‐Fernández, Sandra Morales, et al.. (2023). Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. Artificial Intelligence in Medicine. 145. 102686–102686. 3 indexed citations
5.
Vega, Miguel, et al.. (2022). Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification. Computerized Medical Imaging and Graphics. 97. 102048–102048. 13 indexed citations
6.
Vega, Miguel, et al.. (2020). A TV-based image processing framework for blind color deconvolution and classification of histological images. Digital Signal Processing. 101. 102727–102727. 16 indexed citations
7.
Boissier, S., B. Épinat, P. Amram, et al.. (2020). First spectroscopic study of ionised gas emission lines in the extreme low surface brightness galaxy Malin 1. Library Open Repository (Universidad Complutense Madrid). 11 indexed citations
8.
Vega, Miguel, et al.. (2020). Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors. Sensors. 20(18). 5308–5308. 2 indexed citations
9.
Boissier, S., A. Boselli, Laura Ferrarese, et al.. (2016). The properties of the Malin 1 galaxy giant disk. Astronomy and Astrophysics. 593. A126–A126. 36 indexed citations
10.
Ruiz, Pablo, et al.. (2013). Image deblurring combining poisson singular integral and total variation prior models. European Signal Processing Conference. 1–5. 3 indexed citations
11.
Mateos, Javier, et al.. (2013). General shearlet pansharpening method using Bayesian inference. 231–235. 5 indexed citations
12.
Mateos, Javier, et al.. (2011). Image prior combination in space-variant blur deconvolution for the dual exposure problem. 408–413. 2 indexed citations
13.
Mateos, Javier, et al.. (2011). Space-variant kernel deconvolution for dual exposure problem. European Signal Processing Conference. 1678–1682. 2 indexed citations
14.
Molina, Rafael, Miguel Vega, Javier Mateos, & Aggelos K. Katsaggelos. (2007). Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images. Applied and Computational Harmonic Analysis. 24(2). 251–267. 47 indexed citations
15.
Molina, Rafael, Miguel Vega, Javier Mateos, & Aggelos K. Katsaggelos. (2006). Hierarchical Bayesian super resolution reconstruction of multispectral images. European Signal Processing Conference. 1–5. 4 indexed citations
16.
Molina, Rafael, Javier Mateos, & Aggelos K. Katsaggelos. (2006). Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation. IEEE Transactions on Image Processing. 15(12). 3715–3727. 147 indexed citations
17.
Segall, C.A., Aggelos K. Katsaggelos, Rafael Molina, & Javier Mateos. (2004). Bayesian Resolution Enhancement of Compressed Video. IEEE Transactions on Image Processing. 13(7). 898–911. 80 indexed citations
18.
Molina, Rafael, Javier Mateos, Aggelos K. Katsaggelos, & Miguel Vega. (2003). Bayesian multichannel image restoration using compound gauss-markov random fields. IEEE Transactions on Image Processing. 12(12). 1642–1654. 43 indexed citations
19.
Mateos, Javier, Aggelos K. Katsaggelos, & Rafael Molina. (2000). A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts. IEEE Transactions on Image Processing. 9(7). 1200–1215. 32 indexed citations
20.
Molina, Rafael, Aggelos K. Katsaggelos, & Javier Mateos. (1999). Bayesian and regularization methods for hyperparameter estimation in image restoration. IEEE Transactions on Image Processing. 8(2). 231–246. 143 indexed citations

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.

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