Image Processing Reference

In-Depth Information

Table 1

Results of the Luminance Estimation

PSNR 36.84

36.74

36.76

36.85 36.66

PSNR of the denoising result using the respective luminance estimation method.

3.2 LPA-ICI for Neighborhood Estimation

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

kernels

with the varying scale
h
is used to find an interval
D
.

(2)

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

norm

of the used kernel:

(3)

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.

Search WWH ::

Custom Search