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Computer Science
Spain
2025

Rafael Molina publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Rafael Molina sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

The last bar groups every scientist with 991 publications or more.

Rafael Molina D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Rafael Molina sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Spain Leader Award
  • 2022 - Research.com Computer Science in Spain Leader Award

Overview

Rafael Molina is affiliated with the University of Granada in Spain and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence, computer vision, and medical imaging applications. Their research spans various subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Radiology, Nuclear Medicine and Imaging, and Neurology.

The scientist's work centers on specialized topics such as AI in cancer detection, digital imaging for blood diseases, and advanced image processing techniques. Molina has also explored machine learning in healthcare and radiomics as applied to medical imaging, with additional research into intracerebral and subarachnoid hemorrhage.

Frequent co-authors collaborating with Molina include Aggelos K. Katsaggelos, Pablo Morales-Álvarez, Fernando Pérez-Bueno, Arne Schmidt, and Miguel López-Pérez.

Rafael Molina's recent publications cover diverse applications of AI and image processing. Notable papers include:

  • Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection, 2020, Computer Methods and Programs in Biomedicine
  • The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review, 2022, IEEE Access
  • Deep Gaussian processes for biogeophysical parameter retrieval and model inversion, 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • Learning from crowds in digital pathology using scalable variational Gaussian processes, 2021, Scientific Reports
  • Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference, 2021, Computer Methods and Programs in Biomedicine

Molina's research has been published in a variety of venues, frequently appearing in arXiv (Cornell University), IEEE Access, Digital Signal Processing, Computer Methods and Programs in Biomedicine, and SSRN Electronic Journal.

Best Publications

  • Bayesian Compressive Sensing Using Laplace Priors

    S.D. Babacan;R. Molina;A.K. Katsaggelos

  • Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods

    Alice Lucas;Michael Iliadis;Rafael Molina;Aggelos K. Katsaggelos

  • A survey of classical methods and new trends in pansharpening of multispectral images

    Israa Amro;Israa Amro;Javier Mateos;Miguel Vega;Rafael Molina

  • Variational Bayesian Super Resolution

    S Derin Babacan;Rafael Molina;Aggelos K Katsaggelos

  • Sparse Bayesian Methods for Low-Rank Matrix Estimation

    S. Derin Babacan;Martin Luessi;Rafael Molina;Aggelos K. Katsaggelos

  • Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

    R. Molina;J. Mateos;A.K. Katsaggelos

  • Variational Bayesian Blind Deconvolution Using a Total Variation Prior

    S.D. Babacan;R. Molina;A.K. Katsaggelos

  • Bayesian and regularization methods for hyperparameter estimation in image restoration

    R. Molina;A.K. Katsaggelos;J. Mateos

  • Super Resolution of Images and Video

    Aggelos Konstantinos Katsaggelos;Rafael Molina;Javier Mateos

  • Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

    S.D. Babacan;R. Molina;A.K. Katsaggelos

  • Bayesian blind deconvolution with general sparse image priors

    S. Derin Babacan;Rafael Molina;Minh N. Do;Aggelos K. Katsaggelos

  • Image restoration in astronomy: a Bayesian perspective

    R. Molina;J. Nunez;F.J. Cortijo;J. Mateos

  • Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

    Alice Lucas;Santiago Lopez-Tapia;Rafael Molina;Aggelos K. Katsaggelos

  • Bayesian resolution enhancement of compressed video

    C.A. Segall;A.K. Katsaggelos;R. Molina;J. Mateos

  • High-resolution images from low-resolution compressed video

    C.A. Segall;R. Molina;A.K. Katsaggelos

  • IEEE International Conference on Image Processing

    T. E. Bishop;R. Molina;James Hopgood

  • Variational Bayesian Image Restoration With a Product of Spatially Weighted Total Variation Image Priors

    G. Chantas;N.P. Galatsanos;R. Molina;A.K. Katsaggelos

  • Bayesian combination of sparse and non-sparse priors in image super resolution

    S. Villena;M. Vega;S. D. Babacan;R. Molina

  • Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

    Alice Lucas;Aggelos K. Katsaggelos;Santiago Lopez-Tapuia;Rafael Molina

  • Total variation blind deconvolution using a variational approach to parameter, image, and blur estimation

    S. Derin Babacan;Rafael Molina;Aggelos K. Katsaggelos

Frequent Co-Authors

Aggelos K. Katsaggelos
Aggelos K. Katsaggelos Northwestern University
Nikolas P. Galatsanos
Nikolas P. Galatsanos University of Ioannina
Gustau Camps-Valls
Gustau Camps-Valls University of Valencia
Antonio M. López
Antonio M. López Autonomous University of Barcelona
David M. Goldenberg
David M. Goldenberg Immunomedics (United States)
Jean-Pierre Mach
Jean-Pierre Mach University of Lausanne
Max M. Burger
Max M. Burger Friedrich Miescher Institute
Paul M. Sondel
Paul M. Sondel University of Wisconsin–Madison
James R. Booth
James R. Booth Vanderbilt University
Fionn Murtagh
Fionn Murtagh University of Huddersfield

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