Pablo Arias

1.0k total citations
34 papers, 507 citations indexed

About

Pablo Arias is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Pablo Arias has authored 34 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 10 papers in Media Technology and 3 papers in Artificial Intelligence. Recurrent topics in Pablo Arias's work include Image and Signal Denoising Methods (19 papers), Advanced Image Processing Techniques (18 papers) and Advanced Image Fusion Techniques (9 papers). Pablo Arias is often cited by papers focused on Image and Signal Denoising Methods (19 papers), Advanced Image Processing Techniques (18 papers) and Advanced Image Fusion Techniques (9 papers). Pablo Arias collaborates with scholars based in France, Spain and United States. Pablo Arias's co-authors include Gabriele Facciolo, Jean‐Michel Morel, Guillermo Sapiro, Vicent Caselles, Thibaud Ehret, Axel Davy, Grégory Randall, V. Caselles, Pablo Sprechmann and Coloma Ballester and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and International Journal of Computer Vision.

In The Last Decade

Pablo Arias

33 papers receiving 487 citations

Peers

Pablo Arias
Comparison fields: 5 of 66
  • Computer Vision and Pattern Recognition 427
  • Media Technology 156
  • Computational Mechanics 73
  • Computer Graphics and Computer-Aided Design 56
  • Biomedical Engineering 33
Mona Mahmoudi United States
Luis Mazorra Spain
Andrés Solé Spain
Mingdeng Cao China
Kolmogorov United States
Hossein Talebi United States
Kaibing Zhang China
Mauricio Delbracio United States
Pravin Bhat United States
Thomas Schoenemann Germany
Mona Mahmoudi United States View profile →
Citations per field, relative to Pablo Arias
Pablo Arias · 1×
Citations per year, relative to Pablo Arias
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Countries citing papers authored by Pablo Arias

Since Specialization
Citations

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

Fields of papers citing papers by Pablo Arias

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pablo Arias

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Arias. A scholar is included among the top collaborators of Pablo Arias 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 Pablo Arias. Pablo Arias 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
# Work Indexed citations
1 6
2 2
3 0
4 3
5 4
6 25
7 9
8
Joint demosaicing and denoising by overfitting of bursts of raw images.
10
9 13
10 5
11 2
12 55
13 7
14 24
15 23
16 10
17 7
18 9
19
Segmentación con información a priori de forma aplicada a Sistema de Valoración Cárnica
1
20 1

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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