Wiener Deconvolution

Wiener Deconvolution is a spectral deconvolution method that belongs to the class of linear inverse filters and is distinguished by its use of a statistical optimization criterion minimizing mean squared error between the estimated and true spectra based on signal and noise power spectral density estimates.

125

Publications

6.9K

Citations

396

Authors

206

Institutions

Publications per year

2017–2026

23

Authors

396

  1. TJ
    T. J. M. Jeurens

    Maastricht University, Netherlands

    2 publications · 30 citations

    2

    h-index

  2. JD
    Jiangxin Dong

    Nanjing University of Science and Technology, China

    2 publications · 140 citations

    2

    h-index

  3. KI
    Kazuo Ishizuka

    HREM Research (Japan), Japan

    2 publications · 27 citations

    2

    h-index

  4. MV
    Mirko van der Baan

    University of Alberta, Canada

    2 publications · 193 citations

    2

    h-index

  5. MS
    Murali Subbarao

    Stony Brook University, United States

    2 publications · 134 citations

    2

    h-index

1–5 of 396

Venues

  1. Geophysics

    7 publications · 413 citations

    7

    h-index

  2. IEEE Transactions on Image Processing

    6 publications · 383 citations

    6

    h-index

  3. Geophysical Prospecting

    4 publications · 172 citations

    4

    h-index

  4. Optics Express

    4 publications · 127 citations

    4

    h-index

  5. Applied Optics

    AO · 3 publications · 53 citations

    3

    h-index

1–5