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Noise

Images are often corrupted by noise, random fluctuations that can be characterized by their probability distribution. Two distributions are commonly used to model noise processes: the Gaussian (normal) distribution and the uniform distribution. While several noise processes affecting digital images can be described well by these distributions, quantum nature of light results in a different kind of noise, photon noise, following a Poisson distribution (see Section TM:2.1.4).



Codelet 2 Digital camera photon noise (../TeMa/R/tm.photonNoise.R)

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Photon noise is (proportionally) more significant in the darker areas of images: we choose a new sample image with a dark background and we apply to it a plausible amount of photon noise for a high ISO value of 3200 (the scale parameter of function tm.addNoise).
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We can visually appreciate the effect of noise with a simple compositing operation:
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and having a detailed look at a sample row of the image
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Figure 2.4: Photon noise is due to the quantum nature of light: as its variance is proportional to the square root of the average number of photons, its relative impact can be reduced by increasing the average number of photons, e.g. using large pixels.
[width=7cm]figures/photonNoiseComposite.jpg
[width=7cm]figures/photonNoiseProfile.jpg


next up previous
Next: Digital imaging artifacts Up: The Imaging Process Previous: Diffraction Effects
Roberto Brunelli 2008-11-25