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Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data. Reporter: 沈廷翰 陳奇業. Poissonian-Gaussian Modeling. : the pixel position in the domain X : the recorded signal : the ideal signal

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Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

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  1. Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data Reporter: 沈廷翰 陳奇業

  2. Poissonian-Gaussian Modeling • : the pixel position in the domain X • : the recorded signal • : the ideal signal • :zero-mean independent random noise with standard deviation equal to 1 • : function of that gives the standard deviation of the overall noise component

  3. Poissonian-Gaussian Modeling

  4. Poissonian-Gaussian Modeling • : Poissonian signal-dependent component • the Poissonian has varying variance that depends on the value of • , • : Gaussian signal-independent component • constant variance equal to

  5. The Algorithm • Our goal is to estimate the function of the observation model from a noisy image • local estimation of multiple expectation/ standard-deviation pairs • global parametric model fitting to these local estimates • Maximum-Likelihood Fitting of a Global Parametric Model

  6. The Algorithm

  7. Poissonian-Gaussian Modeling • Wavelet approximation, restricted on the set of smoothness

  8. Poissonian-Gaussian Modeling • detail coefficients, restricted on the set of smoothness

  9. Poissonian-Gaussian Modeling • two level-sets, • : allowed deviation

  10. Poissonian-Gaussian Modeling

  11. Poissonian-Gaussian Modeling • Two segments S obtained for = 0.01 (left) and = 0.0001(right). • The value of is the same for both segments

  12. The Algorithm • The solid line shows the maximum-likelihood estimate of the true standard-deviation function • Estimates the parameters of the noise

  13. The Algorithm • posterior likelihood

  14. Conclusion • Utilizes a special ML fitting of the parametric model on a collection of local wavelet-domain estimates of mean and standard-deviation

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