Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
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Transcript of Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-
Image Raw-DataReporter:沈廷翰 陳奇業
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
Poissonian-Gaussian Modeling
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
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
The Algorithm
Poissonian-Gaussian Modeling
• Wavelet approximation , restricted on the set of smoothness
Poissonian-Gaussian Modeling
• detail coefficients , restricted on the set of smoothness
Poissonian-Gaussian Modeling
• two level-sets , • : allowed deviation
Poissonian-Gaussian Modeling
Poissonian-Gaussian Modeling
• Two segments S obtained for = 0.01 (left) and = 0.0001(right).
• The value of is the same for both segments
The Algorithm
• The solid line shows the maximum-likelihood estimate of the true standard-deviation function
• Estimates the parameters of the noise
The Algorithm
• posterior likelihood
Conclusion
• Utilizes a special ML fitting of the parametric model on a collection of local wavelet-domain estimates of mean and standard-deviation