Image Processing Reference
In-Depth Information
Table 1
Results of the Luminance Estimation
Gaussian h L [ 13 ] Virtual ADA LSLCD [ 13 ]
PSNR 36.84
36.85 36.66
PSNR of the denoising result using the respective luminance estimation method.
3.2 LPA-ICI for Neighborhood Estimation
Once we obtained a continuous luminance estimation we can apply the LPA-ICI method [ 7 ]
to find the dimension of the local homogenous neighborhood. The LPA-ICI method chooses
a polynomial model (LPA) of a certain scale. Based on the ICI rule, the scale of the model is
chosen and this scale defines the extent of a shape around each pixel, in which no singularities
or discontinuities are present.
The LPA-ICI method is applied in eight directions. In each direction θ k a set of directional
with the varying scale h is used to find an interval D .
Γ > 0 is a tuning parameter, which adjusts the size of the interval. The standard deviation of
the estimate
is calculated by multiplying the standard deviation σ of the input with the
of the used kernel:
The standard deviation of the input, σ , is calculated using Equation (1) with the signal value x
estimated by using the noisy observation, thus the raw data pixel value. This is a very simple
estimate, but we found that the improvement using a beter approximation is marginal.
The largest possible scale h i is chosen using the ICI rule.
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