Abstract
Abstract This article deals with an original method to estimate the noise introduced by optical imaging systems, such as CCD cameras. The power of the signal-dependent photon noise is decoupled from the power of the signal-independent electronic noise. The method relies on the multivariate regression of sample mean and variance. Statistically similar image pixels, not necessarily connected, produce scatterpoints that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a commercial CCD camera highlight the accuracy of the proposed method and its applicability to separate R–G–B components that have been corrected for the nonlinear effects of the camera response function, but not yet interpolated to the the full size of the mosaiced R–G–B image.
Highlights
Whenever the assumption of additive white Gaussian noise (AWGN) no longer holds, noise modeling, and estimation becomes a preliminary step of the most advanced image analysis and interpretation systems
Modern CCD color cameras produce corrected R–G–B images dominated by opto-electronic noise, a mixture of signal-dependent photon noise and signal-independent electronic noise
The parameters of the noise model can be measured on a single image by means of an original unsupervised procedure relying on a bivariate linear regression of local mean and variance
Summary
Whenever the assumption of additive white Gaussian noise (AWGN) no longer holds, noise modeling, and estimation becomes a preliminary step of the most advanced image analysis and interpretation systems. On CRF-corrected data, which are much more available and widespread (they might be in principle obtained by properly decimating the demosaiced R–G–B image) the optoelectronic noise model holds on the whole dynamic range and can be more estimated. Other authors develop their analysis in a local mean versus standard deviation space, which makes hard to devise a specific parametric. We develop our model in the local mean versus variance space, in which a nearly linear relation can be recognized and exploited to obtain the noise parameters. Where fis obtained by averaging the observed noisy image, the noise being zero-mean and the average local variance of f is assumed to be negligible, i.e., (f2) ≈ (f)
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