Total Variation Denoising

Total Variation Denoising is a variational image filtering method that minimizes the total variation of the image while preserving edges, thereby effectively reducing noise by favoring piecewise-smooth solutions.

515

Publications

64.1K

Citations

1.2K

Authors

527

Institutions

Publications per year

2017–2026

196

Variants

Rof ModelRudin-osher-fatemi ModelTv Denoising

Authors

1.2K

  1. TF
    Tony F. Chan

    University of California, Los Angeles, United States

    15 publications · 5.5K citations

    15

    h-index

  2. IS
    Ivan Selesnick

    New York University, United States

    10 publications · 916 citations

    10

    h-index

  3. DN
    Dang N. H. Thanh

    University of Economics Ho Chi Minh City, Vietnam

    8 publications · 289 citations

    8

    h-index

  4. VB
    V. B. Surya Prasath

    Cincinnati Children's Hospital Medical Center, United States

    7 publications · 279 citations

    7

    h-index

  5. KC
    Ke Chen

    University of Liverpool, United Kingdom

    7 publications · 327 citations

    7

    h-index

1–5 of 1.2K

Venues

  1. IEEE Transactions on Image Processing

    29 publications · 6K citations

    27

    h-index

  2. SIAM Journal on Imaging Sciences

    SIIMS · 21 publications · 2.4K citations

    20

    h-index

  3. 15

    h-index

  4. Journal of Mathematical Imaging and Vision

    14 publications · 937 citations

    13

    h-index

  5. IEEE Signal Processing Letters

    13 publications · 1.2K citations

    12

    h-index

1–5