Blind Deconvolution

Blind Deconvolution is an inverse signal processing technique that recovers an original signal or image from its convoluted observation without prior knowledge of either the input signal or the convolutional kernel, distinguishing itself from conventional deconvolution methods by simultaneously estimating both the latent source and the blurring operator.

1K

Publications

79.8K

Citations

2.1K

Authors

867

Institutions

Publications per year

2017–2026

247

Variants

Blind Signal Deconvolution

Authors

2.1K

  1. AC
    Andrzej Cichocki

    RIKEN Center for Brain Science, Japan

    18 publications · 1.5K citations

    14

    h-index

  2. AK
    Aggelos K. Katsaggelos

    Northwestern University, United States

    13 publications · 769 citations

    13

    h-index

  3. RM
    Rafael Molina

    Universidad de Granada, Spain

    14 publications · 776 citations

    12

    h-index

  4. TT
    Torfinn Taxt

    University of Bergen, Norway

    11 publications · 449 citations

    11

    h-index

  5. YB
    Yoram Bresler

    University of Illinois Urbana-Champaign, United States

    11 publications · 468 citations

    11

    h-index

1–5 of 2.1K

Venues

  1. IEEE Transactions on Image Processing

    51 publications · 5.3K citations

    35

    h-index

  2. IEEE Transactions on Signal Processing

    44 publications · 2.3K citations

    26

    h-index

  3. Signal Processing

    35 publications · 4.6K citations

    21

    h-index

  4. Journal of the Optical Society of America A

    24 publications · 2.1K citations

    19

    h-index

  5. IEEE Transactions on Information Theory

    20 publications · 2.2K citations

    16

    h-index

1–5