Variational Reconstruction

Variational Reconstruction is a mathematical framework within image reconstruction that formulates the recovery of images from incomplete or noisy data as an optimization problem constrained by a priori regularity conditions, distinguished from other reconstruction methods by its reliance on minimizing an energy functional that balances data fidelity and regularization.

132

Publications

7.3K

Citations

325

Authors

161

Institutions

Publications per year

2017–2026

40

Authors

325

  1. MU
    Michaël Unser

    École Polytechnique Fédérale de Lausanne, Switzerland

    10 publications · 294 citations

    10

    h-index

  2. SM
    Simon Masnou

    Institut Camille Jordan, France

    4 publications · 219 citations

    4

    h-index

  3. KB
    Kristian Bredies

    University of Graz, Austria

    4 publications · 226 citations

    4

    h-index

  4. JK
    Jan Kybic

    Czech Technical University in Prague, Czechia

    4 publications · 140 citations

    4

    h-index

  5. CS
    Carola‐Bibiane Schönlieb

    University of Cambridge, United Kingdom

    3 publications · 371 citations

    3

    h-index

1–5 of 325

Venues

  1. Journal of Mathematical Imaging and Vision

    9 publications · 964 citations

    9

    h-index

  2. IEEE Transactions on Image Processing

    8 publications · 547 citations

    9

    h-index

  3. SIAM Journal on Imaging Sciences

    SIIMS · 7 publications · 260 citations

    7

    h-index

  4. Inverse Problems

    6 publications · 271 citations

    6

    h-index

  5. IEEE Transactions on Signal Processing

    4 publications · 142 citations

    4

    h-index

1–5