Optimization-based Reconstruction

Optimization-Based Reconstruction is an inverse problem solving framework that formulates the reconstruction of an unknown signal or parameter field as a mathematical optimization task, where the solution is determined by minimizing a cost functional composed of data fidelity and regularization terms.

248

Publications

31.7K

Citations

686

Authors

332

Institutions

Publications per year

2017–2026

85

Authors

686

  1. EY
    Emil Y. Sidky

    University of Chicago, United States

    9 publications · 504 citations

    9

    h-index

  2. XP
    Xiaochuan Pan

    University of Chicago, United States

    9 publications · 477 citations

    9

    h-index

  3. EJ
    Emmanuel J. Candès

    Stanford University, United States

    5 publications · 16.3K citations

    5

    h-index

  4. MN
    Mila Nikolova

    Centre National de la Recherche Scientifique, France

    5 publications · 570 citations

    5

    h-index

  5. ZZ
    Zheng Zhang

    University of Chicago, United States

    5 publications · 248 citations

    5

    h-index

1–5 of 686

Venues

  1. Inverse Problems

    14 publications · 585 citations

    13

    h-index

  2. IEEE Transactions on Signal Processing

    11 publications · 2.2K citations

    10

    h-index

  3. Physics in Medicine and Biology

    9 publications · 503 citations

    9

    h-index

  4. Optics Express

    6 publications · 865 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 5 publications · 201 citations

    5

    h-index

1–5