Computer Vision

2014 Edition
| Editors: Katsushi Ikeuchi

Photon, Poisson Noise

  • Samuel W. Hasinoff
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-31439-6_482

Synonyms

Related Concepts

Definition

Photon noise, also known as Poisson noise, is a basic form of uncertainty associated with the measurement of light, inherent to the quantized nature of light and the independence of photon detections. Its expected magnitude is signal dependent and constitutes the dominant source of image noise except in low-light conditions.

Background

Image sensors measure scene irradiance by counting the number of discrete photons incident on the sensor over a given time interval. In digital sensors, the photoelectric effect is used to convert photons into electrons, whereas film-based sensors rely on photosensitive chemical reactions. In both cases, the independence of random individual photon arrivals leads to photon noise, a signal-dependent form of uncertainty that is a property of the underlying signal itself.

In computer vision, a widespread approximation is to model image noise as signal independent, often using a...

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Agrawal A, Raskar R (2009) Optimal single image capture for motion deblurring. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Miami, pp 2560–2567Google Scholar
  2. 2.
    Foi A, Trimeche M, Katkovnik V, Egiazarian K (2008) Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans Image Process 17(10):1737–1754MathSciNetCrossRefGoogle Scholar
  3. 3.
    Granados M, Adjin B, Wand M, Theobalt C, Seidel H-P, Lensch Hendrik PA (2010) Optimal HDR reconstruction with linear digital cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, pp 215–222Google Scholar
  4. 4.
    Hasinoff SW, Kutulakos KN, Durand F, Freeman WT (2009) Time-constrained photography. In: Proceedings of the IEEE international conference on computer vision, Kyoto, pp 333–340Google Scholar
  5. 5.
    Hasinoff SW, Durand F, Freeman WT (2010) Noise-optimal capture for high dynamic range photography. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, pp 553–560Google Scholar
  6. 6.
    Healey GE, Kondepudy R (1994) Radiometric CCD camera calibration and noise estimation. IEEE Trans Pattern Anal Mach Intell 16(3):267–276CrossRefGoogle Scholar
  7. 7.
    Hwang Y, Kim J-S, Kweon I-S (2007) Sensor noise modeling using the Skellam distribution: application to the color edge detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Minneapolis, pp 1–8Google Scholar
  8. 8.
    Liu C, Szeliski R, Kang SB, Lawrence Zitnick C, Freeman WT (2008) Automatic estimation and removal of noise from a single image. IEEE Trans Pattern Anal Mach Intell 30(2):299–314CrossRefGoogle Scholar
  9. 9.
    Prucnal PR, Saleh BEA (1981) Transformation of image-signal-dependent noise into image-signal-independent noise. Opt Lett 6(7):316–318CrossRefGoogle Scholar
  10. 10.
    Treibitz T, Schechner YY (2009) Polarization: beneficial for visibility enhancement? In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Miami, pp 525–532Google Scholar
  11. 11.
    Vahlbruch H, Mehmet M, Chelkowski S, Hage B, Franzen A, Lastzka N, Goßler S, Danzmann K, Schnabel R (2008) Observation of squeezed light with 10-dB quantum-noise reduction. Phys Rev Lett 100(3):033602CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Samuel W. Hasinoff
    • 1
  1. 1.Google, Inc.Mountain ViewUSA