Sensor Noise Model

A Sensor Noise Model is a computational framework that characterizes the statistical deviations and distortions introduced by a physical imaging sensor during image acquisition, distinguishing itself from general noise models by specifically simulating the idiosyncratic noise patterns—such as photon shot noise, readout noise, and pixel non-uniformity—encountered in real-world vision systems, thereby enabling more robust sim-to-real transfer in vision-based machine learning applications.

468

Publications

27.2K

Citations

1.6K

Authors

566

Institutions

Publications per year

2017–2026

135

Authors

1.6K

  1. RS
    Roland Siegwart

    ETH Zurich, Switzerland

    5 publications · 165 citations

    5

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  2. MN
    M. Nofrarías

    Max Planck Institute for Gravitational Physics, Germany

    5 publications · 157 citations

    5

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  3. YF
    Ying Fu

    Beijing Institute of Technology, China

    5 publications · 382 citations

    5

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  4. GH
    Gerhard Heinzel

    Max Planck Institute for Gravitational Physics, Germany

    5 publications · 157 citations

    5

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  5. SV
    S. Vitale

    University of Trento, Italy

    5 publications · 291 citations

    5

    h-index

1–5 of 1.6K

Venues

  1. Sensors

    13 publications · 354 citations

    12

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  2. 12

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  3. IEEE Sensors Journal

    11 publications · 464 citations

    11

    h-index

  4. 9

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  5. 9

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1–5