Computer Vision

2014 Edition
| Editors: Katsushi Ikeuchi

Saturation (Imaging)

  • Samuel W. Hasinoff
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-31439-6_483

Synonyms

Related Concepts

Definition

In imaging, saturation is a type of distortion where the recorded image is limited to some maximum value, interfering with the measurement of bright regions of the scene.

Background

The role of a sensor element is to measure incident irradiance and record that quantity as an image intensity value. However, physical constraints limit the maximum irradiance that can be measured for a given camera setting. In the absence of noise, the mapping from irradiance to image intensity is fully described by the radiometric response function, a monotonically increasing function whose range is restricted by the maximum irradiance. Pixels whose intensity corresponds to this maximum are known as saturated.

Saturated pixels contain less information about the scene than other pixels. While non-saturated pixels can be related to the incident irradiance by applying the inverse of the radiometric response function, saturated...

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

References

  1. 1.
    . Abel JS, Smith JO (1991) Restoring a clipped signal. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, Toronto, pp 1745–1748Google Scholar
  2. 2.
    . Criminisi A, Pérez P, Toyama K (2003) Object removal by exemplar-based inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Madison, vol 2, pp 721–728Google Scholar
  3. 3.
    . Debevec PE, Malik J (1997) Recovering high dynamic range radiance maps from photographs. In: Proceedings of the ACM SIGGRAPH, Los Angeles, pp 369–378Google Scholar
  4. 4.
    Foi A (2009) Clipped noisy images: heteroskedastic modeling and practical denoising. Signal Process 89(12): 2609–2629CrossRefzbMATHGoogle Scholar
  5. 5.
    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
  6. 6.
    . Granados M, Adjin B, Wand M, Theobalt C, Seidel H-P, Lensch HPA (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
  7. 7.
    . Guo D, Cheng Y, Zhuo S, Sim T (2010) Correcting over-exposure in photographs. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, pp 515–521Google Scholar
  8. 8.
    . Hasinoff SW, Durand F, Freeman WT (2010) Noise-optimal capture for high dynamic range photography. In: Proceedings IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, pp 553–560Google Scholar
  9. 9.
    Hunt RWG (2004) The reproduction of colour, 6th edn. Wiley, HobokenCrossRefGoogle Scholar
  10. 10.
    . Kavusi S, El Gamal A (2004) Quantitative study of high dynamic range image sensor architectures. In: Proceedings of the SPIE electronic imaging conference, San Jose, CA, vol 5301, pp 264–275Google Scholar
  11. 11.
    Liu C, Szeliski R, Kang SB, Zitnick CL, Freeman WT (2008) Automatic estimation and removal of noise from a single image. IEEE Trans Pattern Anal Mach Intell 30(2):299–314CrossRefGoogle Scholar
  12. 12.
    Masood SZ, Zhu J, Tappen MF (2009) Automatic correction of saturated regions in photographs using cross-channel correlation. Proc Pac Graph 28:1861–1869Google Scholar
  13. 13.
    Narasimhan SG, Nayar SK (2005) Enhancing resolution along multiple imaging dimensions using assorted pixels. IEEE Trans Pattern Anal Mach Intell 27(4):518–530CrossRefGoogle Scholar
  14. 14.
  15. 15.
    . Rempel AG, Trentacoste M, Seetzen H, Young HD, Heidrich W, Whitehead L, Ward G (2007) Ldr2hdr: on-the-fly reverse tone mapping of legacy video and photographs. ACM Trans Graph 26Google Scholar
  16. 16.
    Sugimoto M (2008) Digital camera. United States Patent 7,468,746, Dec 2008Google Scholar
  17. 17.
    . Wang L, Wei L-Y, Zhou K, Guo B, Shum H-Y (2007) High dynamic range image hallucination. In: Proceedings of the eurographics symposium on rendering, Grenoble, pp 1–7Google Scholar
  18. 18.
    . Wetzstein G, Ihrke I, Heidrich W (2010) Sensor saturation in fourier multiplexed imaging. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, pp 545–552Google Scholar
  19. 19.
    Zhang X, Brainard DH (2004) Estimation of saturated pixel values in digital color imaging. J Opt Soc Am A 21(12):2301–2310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Samuel W. Hasinoff
    • 1
  1. 1.Google, Inc.Mountain View, CAUSA