Optics and Photonics Journal, 2013, 3, 318-321
doi:10.4236/opj.2013.32B074 Published Online June 2013 (http://www.scirp.org/journal/opj)
Image Correction Method of Color Line-Scan System
Zhen-long Chen, Yu-tang Ye, Yun-cen Song, Ying Luo, Lin Liu, Juan-xiu Liu
School of Opto-electronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
Email: firstname.lastname@example.org, email@example.com
As the development of machin e v ision techno logy, th e co lor line-scan syste m is widely ap p lied in th e on-lin e insp ection.
Due to the non-uniform gray scale and color distortion of the image acquired by the system, the image correction is
needed to reduce the problem of image processing and the stability system. Based on reasons mentioned above, a
method that using polynomial fitting to correct the image is presented to solve the problem in this paper. The method
has been used in the automatic optical inspection of PCB, and has been proved to be effective. So this method will have
a potential application to the development of the color line-scan machine vision system.
Keywords: Machin e Vision; Autom atic Optical Inspection (AOI); Color Line-scan System; White Balance; Flat-field
With the development of machine vision and automated
optical inspection technology, system application (such
as inspection of PCB appearance defect, color print de-
fect detection, etc...) based on the color line-scan tech-
nology is increasingly widespread. In the color line-scan
visual system, the non-uniform distribution of optical
intensity, the vignetting of optical lens, and the inho-
mogeneity of CCD camera, will result in non-uniform
grayscale and color distortion of the image acquired by
the system. This can bring difficulties for the back-end
image processing, so that it can have some impacts on
2. Image Quality Correcting Method
Color line-scan system structure is shown in Figure 1.
Due to the correction method of RGB channel is basi-
cally the same, we take R channel processing as an ex-
ample flat-field correction method for color image de-
Figure 1. Structure of color line-scan system.
2.1. Color Correction
For Cb and Cr reflecting the luminance and chrominance
of red and blue channel respectively, so the color correc-
tion need to change the image from RGB to YCbCr
space. The color space transform method is shown in the
0.299 0.587 0.114
In this method, the scope of Cr and Cb is from -127 to
127. After space transform, according to the color tem-
perature of YCbCr space, the color cast is obtained. Base
on the color cast, Cb and Cr can be corrected the best
value on the basis of channel gain( Cb and Cr of white
color is zero, so channel gain is getting the parameters
which can adjust Cb and Cr to zero or close to zero).
2.2. Noise Eliminating
We use color linear CCD image acquisition system
shown in Figure 1 to do image acquisition for the stan-
dard whiteboard. In the standard whiteboard image cap-
ture experiments, we collected 1000 lines image data as
uniform field images, as shown in Figure 2.
In order to eliminate random noise, extract gray scale
information of the R channel of the color image from
Figure 2, and do average processing for the pixel values
in the image’s vertical direction (i.e. the relative move-
ment direction compare colored line array CCD camera
with the scan target), obtain gradation distribution shown
Copyright © 2013 SciRes. OPJ
Z.-L. CHEN ET AL. 319
in Figure 3. Seen from the Figure 3, because of the lens
vignetting effect, the gradation is high in the middle and
low in the ends and appears actuate distribution in the
horizontal direction (i.e. the colored line array CCD
camera pixel arrangement direction). At the same time,
due to dark current and other factors, cause that there are
many spikes noise in the vertical direction, as shown in
Since the noise is very serious, for further analysis,
firstly we need do denoising for the image. Daubechies-4
wavelet was used in experiments  to implement fifth
decomposition. After several attempts at commissioning
site, we get the final threshold value of each layer is set
to about 4. The fine structure in Figure 3 is substantially
filtered out and we ob tain the final grayscale distribution
shows in Figure 4.
Figure 2. The white panel's image grabbed by color line-scan
Figure 3. The gray distribution of the white panel's R channel.
Figure 4. The gray distribution the white panel's after
2.3. Gray level Correction
In this part, fifth-order polynomial is used to do the fit-
ting of RGB channel's gray distribution which is after
denoising. In the polynomial, x is the location of pixel,
and y is the gray value of the pixel at x. The correction
method is as follows.
If given data are consistent with the fifth-order poly-
nomial as the formula (2):
and the fitting points are (x1,y1 ),(x2 ,y2) and so on , so the
aim of least square method is getting a set of parameters
(named a5, a4, a3, a2, a1 and a0) which let sum of square
of deviations to the minimum. The calculation method of
sum of square of deviations is as formula (3):
According to matrix theory, obtaining the solution of
least square method can use the formula (4).
For the number of equations is greater than the number
of unknowns, the so lution of the matrix theory can no t be
obtained, so only least square solution can be obtained.
So the least square solution of multinomial coefficients is
shown in formula(5).
Since three RGB channels of color linear CCD are
relatively independent, each channel was processed as
above, which can get the parameters of the actual gray
level correction and the deviation value for each channel
at each pixel location. Then, the correction parameters
for each channel is applied to the corresponding channel
of the actual image, it can realize a flat field correction
processing of the color image.
In this paper, in order to avoid the noise’s impact on
the gradation correctio n, firstly denoise the unifo rm field,
then do polynomial fitting, we get the fitting curve of
gray distribution as Figure 5. Using the correction pa-
rameters to correct the image shown in Figure 2, the
processed gray distribution of R channel is shown in
Figure 6. According to Figure 6 we can know, the gray
distribution of the white panel's R channel is improved,
and more suitable for threshold segmentation and edge
3. Experimental Results
Apply the parameters obtained from the correction proc-
Copyright © 2013 SciRes. OPJ
Z.-L. CHEN ET AL.
ess to the actual collection of the PCB color image, the
effect of the correction around the PCB was shown in
Figure 7(a), (b) (the direction of the scanning line is the
image height direction). The gray level histogram before
and after corrections of R channel correspond to Figure
7 is shown in Figure 8. Figure 8(a) shows that before
the correction the image grayscale distribution has more
peaks and the difference is not obvious. Image process-
ing like threshold segmentation is very difficult. With
reference to Figure 7(a), we can get that this is because
the whole image is dim, and more to the direction of the
large field angle, the lower the pixel value will be. After
corrections, the grayscale distribution tends to be high,
low ends and concentrated peak became distinct, which
is consistent with the entire image brightness and uni-
formity improved shown in Figure 7(b). After using the
correction method proposed in this paper, the image
quality is improved. More reliable image sources for im-
age segmentation, cluster analysis in the subsequent
processing of the system are provided. And it’s condu-
cive to the stability of the system.
Figure 5. The fitting curve of gray distribution at R channel.
Figure 6. The processed gray distribution of R channel.
(a) source image
Figure 7. The comparison between before and after proce ss
the PCB image.
(a) the density histogram of source image
(b) density histogram of processed image
Figure 8. The the R channel density histogram of before
and after processing PCB image .
According to the characteristics of the actual build color
linear CCD image acquisition system, this article pro-
posed a flat-field correction method of the color line im-
age. Although processing steps of correction process is a
bit much, once get the system correction factor, it can be
applied in all acquired PCB images by looking up tables
without taking up processing resources of the detection
system and adjusting the hardware system. It’s very
valuable for the actual application of PCB detection sys-
The method has been applied to “the superficial defect
of smart printed circuit board (PCB) inspection machine”.
It has been put into production. Experimental results
show that the image correction method proposed in this
article has a good practical application to enhance the
Copyright © 2013 SciRes. OPJ
Z.-L. CHEN ET AL.
Copyright © 2013 SciRes. OPJ
image quality and system stability of the PCB color
line0scan machine vision systems, and also has a very
good reference value for research and development of the
other color line-scan machine vision system.
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