Background Normalization

Background Normalization is a methodological technique in signal or image processing that standardizes background data across samples or measurements to minimize non-biological or non-informative variability while preserving meaningful signal differences.

149

Publications

15.9K

Citations

557

Authors

247

Institutions

Publications per year

2017–2026

34

Authors

557

  1. TB
    Thierry Bouwmans

    La Rochelle Université, France

    6 publications · 1.4K citations

    6

    h-index

  2. ZW
    Zhijin Wu

    Brown University, United States

    3 publications · 1.9K citations

    3

    h-index

  3. JL
    Jiamin Liu

    National Institutes of Health Clinical Center, United States

    3 publications · 89 citations

    3

    h-index

  4. RA
    Rafael A. Irizarry

    Johns Hopkins University, United States

    3 publications · 1.9K citations

    3

    h-index

  5. YZ
    Ying Zhuge

    National Cancer Institute, United States

    2 publications · 75 citations

    2

    h-index

1–5 of 557

Venues

  1. Pattern Recognition Letters

    4 publications · 223 citations

    4

    h-index

  2. Machine Vision and Applications

    4 publications · 167 citations

    4

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 155 citations

    4

    h-index

  4. BMC Bioinformatics

    3 publications · 838 citations

    3

    h-index

  5. 3

    h-index

1–5