IEEE Trans Pattern Anal Mach Intell - Difference-Based Image Noise Modeling Using Skellam Distribution.

Tópicos

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Resumo

By the laws of quantum physics, pixel intensity does not have a true value, but should be a random variable. Contrary to the conventional assumptions, the distribution of intensity may not be an additive Gaussian. We propose to directly model the intensity difference and show its validity by an experimental comparison to the conventional additive model. As a model of the intensity difference, we present a Skellam distribution derived from the Poisson photon noise model. This modeling induces a linear relationship between intensity and Skellam parameters, while conventional variance computation methods do not yield any significant relationship between these parameters under natural illumination. The intensity-Skellam line is invariant to scene, illumination, and even most of camera parameters. We also propose practical methods to obtain the line using a color pattern and an arbitrary image under natural illumination. Because the Skellam parameters that can be obtained from this linearity determine a noise distribution for each intensity value, we can statistically determine whether any intensity difference is caused by an underlying signal difference or by noise. We demonstrate the effectiveness of this new noise model by applying it to practical applications of background subtraction and edge detection.

Resumo Limpo

law quantum physic pixel intens true valu random variabl contrari convent assumpt distribut intens may addit gaussian propos direct model intens differ show valid experiment comparison convent addit model model intens differ present skellam distribut deriv poisson photon nois model model induc linear relationship intens skellam paramet convent varianc comput method yield signific relationship paramet natur illumin intensityskellam line invari scene illumin even camera paramet also propos practic method obtain line use color pattern arbitrari imag natur illumin skellam paramet can obtain linear determin nois distribut intens valu can statist determin whether intens differ caus under signal differ nois demonstr effect new nois model appli practic applic background subtract edg detect

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