World's Best Scientists 2026 revealed!
Jose M. Bioucas-Dias

Jose M. Bioucas-Dias

Award Badge
Computer Science
Portugal
2025

D-Index & Metrics

Computer Science

D-Index
79
Citations
40253
World Ranking
1119
National Ranking
1

Jose M. Bioucas-Dias 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 Jose M. Bioucas-Dias 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+

This scientist: 331 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Jose M. Bioucas-Dias 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 Jose M. Bioucas-Dias 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+

This scientist: 79 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in Portugal Leader Award
  • 2023 - Research.com Computer Science in Portugal Leader Award
  • 2022 - Research.com Computer Science in Portugal Leader Award

Overview

Jose M. Bioucas-Dias was affiliated with Instituto Superior Técnico in Portugal. Their research spanned several interdisciplinary fields, primarily centered on engineering, computer science, and medicine. The scientist's work contributed notably to subfields such as media technology, computer vision and pattern recognition, computational mechanics, biomedical engineering, and signal processing.

Their scholarly output included focus areas encompassing remote-sensing image classification, image and signal denoising methods, advanced image fusion techniques, sparse and compressive sensing techniques, blind source separation techniques, spectroscopy and chemometric analyses, and remote sensing and land use.

Frequent coauthors collaborated with Jose M. Bioucas-Dias on various publications, including Lina Zhuang, Xiyou Fu, Michael K. Ng, Mário A. T. Figueiredo, and Sérgio G Pinto.

The scientist published regularly in notable venues, with multiple papers appearing in the IEEE Transactions on Geoscience and Remote Sensing, Cureus, IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and IEEE Multimedia.

Selected recent papers included:

  • Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations, 2021, IEEE Transactions on Geoscience and Remote Sensing
  • Adaptive Hyperspectral Mixed Noise Removal, 2021, IEEE Transactions on Geoscience and Remote Sensing
  • Hy-Demosaicing: Hyperspectral Blind Reconstruction From Spectral Subsampling, 2021, IEEE Transactions on Geoscience and Remote Sensing
  • Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • Block-Gaussian-Mixture Priors for Hyperspectral Denoising and Inpainting, 2020, IEEE Transactions on Geoscience and Remote Sensing

Best Publications

  • Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

    J. M. Bioucas-Dias;A. Plaza;N. Dobigeon;M. Parente

  • Vertex component analysis: a fast algorithm to unmix hyperspectral data

    Unknown

  • A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration

    J.M. Bioucas-Dias;M.A.T. Figueiredo

  • Hyperspectral Remote Sensing Data Analysis and Future Challenges

    J. M. Bioucas-Dias;A. Plaza;G. Camps-Valls;P. Scheunders

  • Fast Image Recovery Using Variable Splitting and Constrained Optimization

    Manya V Afonso;José M Bioucas-Dias;Mário A T Figueiredo

  • Hyperspectral Subspace Identification

    J.M. Bioucas-Dias;J.M.P. Nascimento

  • An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

    M V Afonso;José M Bioucas-Dias;Mário A T Figueiredo

  • Sparse Unmixing of Hyperspectral Data

    Marian-Daniel Iordache;J M Bioucas-Dias;A Plaza

  • Hyperspectral Pansharpening: A Review

    Laetitia Loncan;Luis B. de Almeida;Jose M. Bioucas-Dias;Xavier Briottet

  • Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

    M.-D Iordache;J. M. Bioucas-Dias;A. Plaza

  • Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

    Jun Li;J. M. Bioucas-Dias;A. Plaza

  • A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization

    Miguel Simoes;Jose Bioucas-Dias;Luis B. Almeida;Jocelyn Chanussot

  • Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing

    Jose M. Bioucas-Dias;Mario A. T. Figueiredo

  • Phase Unwrapping via Graph Cuts

    J.M. Bioucas-Dias;G. Valadao

  • Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation

    Qi Wei;Jose Bioucas-Dias;Nicolas Dobigeon;Jean-Yves Tourneret

  • Majorization–Minimization Algorithms for Wavelet-Based Image Restoration

    M.A.T. Figueiredo;J.M. Bioucas-Dias;R.D. Nowak

  • Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning

    Jun Li;José M Bioucas-Dias;Antonio Plaza

  • Generalized Composite Kernel Framework for Hyperspectral Image Classification

    Jun Li;Prashanth Reddy Marpu;Antonio Plaza;Jose M. Bioucas-Dias

  • Collaborative Sparse Regression for Hyperspectral Unmixing

    Marian-Daniel Iordache;Jose M. Bioucas-Dias;Antonio Plaza

  • Restoration of Poissonian Images Using Alternating Direction Optimization

    M A T Figueiredo;J M Bioucas-Dias

  • Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization

    Shutao Li;Renwei Dian;Leyuan Fang;José M. Bioucas-Dias

  • A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

    Wing-Kin Ma;Jose M. Bioucas-Dias;Tsung-Han Chan;Nicolas Gillis

  • A variable splitting augmented Lagrangian approach to linear spectral unmixing

    Jose M. Bioucas-Dias

Frequent Co-Authors

Antonio Plaza
Antonio Plaza University of Extremadura
Mário A. T. Figueiredo
Mário A. T. Figueiredo Instituto Superior Técnico
Nicolas Dobigeon
Nicolas Dobigeon National Polytechnic Institute of Toulouse
Jocelyn Chanussot
Jocelyn Chanussot Grenoble Alpes University
Jelena Kovacevic
Jelena Kovacevic New York University
Jean-Yves Tourneret
Jean-Yves Tourneret National Polytechnic Institute of Toulouse
Vladimir Katkovnik
Vladimir Katkovnik Tampere University
Gerald S. Buller
Gerald S. Buller Heriot-Watt University
Wing-Kin Ma
Wing-Kin Ma Chinese University of Hong Kong
Paul D. Gader
Paul D. Gader University of Florida

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