[IPOL announce] new article: Analysis and Extension of the Percentile Method, Estimating a Noise Curve from a Single Image

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2013/90/

Analysis and Extension of the Percentile Method, Estimating a Noise 
Curve from a Single Image
by Miguel Colom, Antoni Buades
Image Processing On Line, vol. 2013, pp. 322--349.
http://dx.doi.org/10.5201/ipol.2013.90

Abstract

Given a white Gaussian noise signal on a sampling grid, its variance can 
be estimated from a small block sample.
However, in natural images we observe the combination of the geometry of 
the scene being photographed and the added noise. In this case, 
estimating directly the standard deviation of the noise from block 
samples is not reliable since the measured standard deviation is not 
explained just by the noise but also by the geometry of the image.
The Percentile method tries to estimate the standard deviation of the 
noise from blocks of a high-passed version of the image and a small 
p-percentile of these standard deviations. The idea behind is that edges 
and textures in a block of the image increase the observed standard 
deviation but they never make it decrease. Therefore, a small percentile 
(0.5%, for example) in the list of standard deviations of the blocks is 
less likely to be affected by the edges and textures than a higher 
percentile (50%, for example). The 0.5%-percentile is empirically proven 
to be adequate for most natural, medical and microscopy images.
The Percentile method is adapted to signal-dependent noise, which is 
realistic with the Poisson noise model obtained by a CCD device in a 
digital camera.


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