tuttle::plugin::imageUtils Namespace Reference

Functions
template<class S_VIEW , class D_VIEW >
D_VIEW &	pseudo_residual (S_VIEW &src, D_VIEW &dst)
	Compute somme pseudo-residuals The pseudo residual r_i of the image Y_i are so thar E[r_i^2] = E[Y_i^2]. This is the 2D pseudo-implementation.
template<class S_VIEW , typename t >
double	variance_mean (S_VIEW &src, t &mean)
	Return the variance and the mean of the image Least Mean of Square.
template<class S_VIEW >
double	noise_variance (S_VIEW &src)
	Robustly estimate the variance of a the noise using the pseudo-residuals.

---

Function Documentation

template<class S_VIEW >

double tuttle::plugin::imageUtils::noise_variance

(

S_VIEW &

src

)

Robustly estimate the variance of a the noise using the pseudo-residuals.

See also:

variance_estimation()

Definition at line 114 of file noiseAnalysis.hpp.

References pseudo_residual(), variance_mean(), and boost::gil::view().

Here is the call graph for this function:

template<class S_VIEW , class D_VIEW >

D_VIEW& tuttle::plugin::imageUtils::pseudo_residual	(	S_VIEW &	src,
		D_VIEW &	dst
	)

Compute somme pseudo-residuals The pseudo residual r_i of the image Y_i are so thar E[r_i^2] = E[Y_i^2]. This is the 2D pseudo-implementation.

Definition at line 20 of file noiseAnalysis.hpp.

Referenced by noise_variance().

template<class S_VIEW , typename t >

double tuttle::plugin::imageUtils::variance_mean	(	S_VIEW &	src,
		t &	mean
	)

Return the variance and the mean of the image Least Mean of Square.

Definition at line 76 of file noiseAnalysis.hpp.

Referenced by noise_variance().