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
(the algorithm of Michael Elad [ 12 ] and MSRCR [ 13 , 14 ] ) as illumination images. Through these
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.
By observing Figure 1 , we find the curve of MSRCR on the figure can be represented by a
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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