Image Reconstruction Algorithms

Image Reconstruction Algorithms is a computational methodology that belongs to the class of image processing techniques and is distinguished by its ability to recover high-fidelity spatial and spectral information from undersampled, noisy, or otherwise degraded measurement data in nonlinear and super-resolution optical microscopy, particularly within the near-infrared regime, through the application of mathematical models and iterative optimization strategies.

3.4K

Publications

238.7K

Citations

10.1K

Authors

2.3K

Institutions

Publications per year

2017–2026

1.1K

Authors

10.1K

  1. KD
    Keith D. Paulsen

    Dartmouth College, United States

    23 publications · 3.2K citations

    22

    h-index

  2. XP
    Xiaochuan Pan

    University of Chicago, United States

    39 publications · 5K citations

    22

    h-index

  3. JA
    Jeffrey A. Fessler

    University of Michigan, United States

    30 publications · 2.5K citations

    20

    h-index

  4. XY
    Xin Yuan

    Westlake University, China

    26 publications · 2.6K citations

    20

    h-index

  5. EY
    Emil Y. Sidky

    University of Chicago, United States

    23 publications · 4.3K citations

    18

    h-index

1–5 of 10.1K

Venues

  1. IEEE Transactions on Medical Imaging

    127 publications · 14.9K citations

    57

    h-index

  2. IEEE Transactions on Image Processing

    89 publications · 9.8K citations

    48

    h-index

  3. Optics Express

    143 publications · 7.8K citations

    47

    h-index

  4. Medical Physics

    115 publications · 7.3K citations

    46

    h-index

  5. Physics in Medicine and Biology

    120 publications · 11.8K citations

    44

    h-index

1–5