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Proceedings Paper

Approximations to camera sensor noise
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Paper Abstract

Noise is present in all image sensor data. Poisson distribution is said to model the stochastic nature of the photon arrival process, while it is common to approximate readout/thermal noise by additive white Gaussian noise (AWGN). Other sources of signal-dependent noise such as Fano and quantization also contribute to the overall noise profile. Question remains, however, about how best to model the combined sensor noise. Though additive Gaussian noise with signal-dependent noise variance (SD-AWGN) and Poisson corruption are two widely used models to approximate the actual sensor noise distribution, the justification given to these types of models are based on limited evidence. The goal of this paper is to provide a more comprehensive characterization of random noise. We concluded by presenting concrete evidence that Poisson model is a better approximation to real camera model than SD-AWGN. We suggest further modification to Poisson that may improve the noise model.

Paper Details

Date Published: 19 February 2013
PDF: 7 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550H (19 February 2013); doi: 10.1117/12.2019212
Show Author Affiliations
Xiaodan Jin, Univ. of Dayton (United States)
Keigo Hirakawa, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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