Image Processing Reference

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tion of high pixels is lower affected by adjacent low pixels, the estimated illumination of low

pixels is higher affected by adjacent high pixels. The distortion of the estimated illumination

leads to the “halo effect.”

3 Analyzing the transformation law and enhancing the

nighttime image

We first transform the original RGB space to HSV space, because processing the color image

directly in RGB space will lead to color distortion. The HSV space is closer to human visual

perception in color perception. Our transformation law is only used in brightness component

of HSV space.

Currently, most algorithms often use the filtering method to estimate the illumination im-

age, and achieve good results. In this article, we use the processing results of some algorithms

two algorithms, we get three images. One is the nightime image and the other two are the cor-

responding illumination images. Their brightness components of HSV space are denoted as
L
,

M
, and
N
. For analyzing the transformation law, we get pixels which value is
i
(0 − 255) from

L
. Then, we have a set of coordinates through known pixels. In the same coordinates, we get

two sets of pixel values from
M
and
N
. The average (
j
,
k
) of these two sets are the correspond-

ing value to
i
.
Figure 1
displays the correspondence between
i
and
j
,
k
. In order to facilitate

observation, we add a linear which is
y
=
x
.

FIGURE 1
The corresponding graphs of pixel values of source images and illumination im-

ages obtained by the algorithm of Michael Elad and MSRCR algorithm. Panels (a-d) are the

processing results of four different pictures.

circular arc. But it is too close to the linear which is
y
=
x
resulting in that the enhanced image

is too bright and loses details seriously. The curve of Michael Elad is roughly like a circular

arc except a small part. It is the reason that the resulting images processed by the algorithm

of Michael Elad have a stronger noise. Overall, we can use a circular arc to represent the rela-

tionship between the input image and illumination image. Obviously, the fitting circular arc

should pass the point (255,255). In order to facilitate the calculation, we use two parameters to

represent the circular arc. One parameter is
x
-coordinate (
x
0
) of the circular center. Another is

the intersection (0,
λ
) of arc and
y
positive axle. According to the nature of the circle, the
y
-co-

ordinate (
y
0
) of the circular center can be expressed as the following equation:

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