Inpainting

Inpainting is a computational image synthesis task that belongs to the broader category of image restoration methods, specifically distinguished by its objective of plausibly reconstructing missing or occluded regions within an image based on learned semantic and structural priors from surrounding context.

1.6K

Publications

143.3K

Citations

4.5K

Authors

1.2K

Institutions

Publications per year

2017–2026

1K

Authors

4.5K

  1. TF
    Tony F. Chan

    University of California, Los Angeles, United States

    18 publications · 4.6K citations

    17

    h-index

  2. GS
    Guillermo Sapiro

    University of Minnesota, United States

    15 publications · 7.3K citations

    15

    h-index

  3. YC
    Yuantao Chen

    Zoomlion (China), China

    14 publications · 1.1K citations

    14

    h-index

  4. WZ
    Wangmeng Zuo

    Harbin Institute of Technology, China

    13 publications · 2.9K citations

    13

    h-index

  5. ZL
    Zhe Lin

    Adobe Systems (United States), United States

    13 publications · 5K citations

    12

    h-index

1–5 of 4.5K

Venues

  1. IEEE Transactions on Image Processing

    80 publications · 9.1K citations

    44

    h-index

  2. arXiv (Cornell University)

    Repository · 150 publications · 8.6K citations

    42

    h-index

  3. 24

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 27 publications · 3.5K citations

    23

    h-index

  5. SIAM Journal on Imaging Sciences

    SIIMS · 25 publications · 1.5K citations

    20

    h-index

1–5