Multiscale Image Representation

Multiscale Image Representation is a computational imaging framework that characterizes visual information across multiple spatial scales by decomposing an image into hierarchical layers, each capturing structural details at a distinct resolution, thereby enabling efficient analysis of spatial frequency content in natural and synthetic scenes.

602

Publications

48.1K

Citations

1.7K

Authors

743

Institutions

Publications per year

2017–2026

187

Authors

1.7K

  1. EP
    Eero P. Simoncelli

    New York University, United States

    9 publications · 2.3K citations

    9

    h-index

  2. TL
    Tony Lindeberg

    KTH Royal Institute of Technology, Sweden

    8 publications · 1.5K citations

    8

    h-index

  3. RF
    Raanan Fattal

    Hebrew University of Jerusalem, Israel

    7 publications · 2.3K citations

    7

    h-index

  4. NA
    Narendra Ahuja

    University of Illinois Urbana-Champaign, United States

    7 publications · 512 citations

    7

    h-index

  5. DL
    David L. Donoho

    Stanford University, United States

    6 publications · 880 citations

    6

    h-index

1–5 of 1.7K

Venues

  1. IEEE Transactions on Image Processing

    41 publications · 4.1K citations

    29

    h-index

  2. 20

    h-index

  3. 17

    h-index

  4. 14

    h-index

  5. ACM Transactions on Graphics

    TOG · 11 publications · 2.2K citations

    11

    h-index

1–5