A Novel Defect Inspection Method for the TFT-LCD Image Based on ...

The light is diffused and concentrated through <strong>the</strong>se sheets.
But <strong>the</strong>re still exist some illumination variations measured
by LU of more than 80%. Above 80% LU is considered
acceptable in industry. The luminance values <strong>for</strong> calculating
<strong>the</strong> LU are acquired from <strong>the</strong> predetermined positions.
Mura defects are darker or brighter than <strong>the</strong>ir neighboring
normal regions in <strong>TFT</strong>-<strong>LCD</strong> images. They have various
shapes and strengths according to <strong>the</strong> cause of generation.
Some of <strong>the</strong>m occur when unexpected <strong>for</strong>eign materials are
inserted into <strong>the</strong> layers in a display panel and <strong>the</strong>y can be
generated due to non-uni<strong>for</strong>m liquid crystal distribution, air
insertion, and partial press by external <strong>for</strong>ces, etc [8].
Because of <strong>the</strong> non-uni<strong>for</strong>m illumination characteristic of <strong>the</strong>
background signal, dark Mura can be brighter than <strong>the</strong>
normal background region. Two of our experimental <strong>TFT</strong>-
<strong>LCD</strong> panel images are shown in Fig. 3(a), (b). Fig. 3(a)
includes 4 White and Dark Mura defects and Fig. 3(b) has
<strong>the</strong> signal generated Muras with various size and strength.
The position of Muras are encircled or marked with arrows.
The line profile of dotted circle and arrow is shown in Fig.3
(c). From <strong>the</strong>se figures, <strong>the</strong> illumination of <strong>TFT</strong>-<strong>LCD</strong> image
shows local and global variations at <strong>the</strong> same time.
3 Proposed <strong>Defect</strong> Detection Algorithm
No
Not Mura
Input <strong>Image</strong>
Discrete Cosine Trans<strong>for</strong>m
Adapted CSF Filtering
Inverse
Discrete Cosine Trans<strong>for</strong>m
Polynomial Regression Analysis
Tri-modal Thresholding
Labeling
L
if C
L
Result <strong>Image</strong>
Mura
Fig.4. The proposed block diagram <strong>for</strong> <strong>TFT</strong>-<strong>LCD</strong> image defect detection
Yes
The flow chart of <strong>the</strong> proposed algorithm is seen in Fig.
4. First, <strong>the</strong> input image is DCT trans<strong>for</strong>med and <strong>the</strong>n
proposed ACSF filter is applied to enlarge <strong>the</strong> Mura signal.
After <strong>the</strong> signal is inverse Discrete Cosine Trans<strong>for</strong>med,
polynomial regression (PR) process is employed <strong>for</strong>
flattening. Then <strong>the</strong> enhanced image is segmented by trimodal
thresholding to find White and Dark Mura at once.
To leave out reliable defects, false defects are eliminated if
<strong>the</strong> mean gray level of a defect does not have much
difference from <strong>the</strong>ir background.
3.1 Adapted CSF in DCT Domain
The human visual system(HVS) has different
frequency sensitivity and that is ma<strong>the</strong>matically represented
as CSF given by <strong>the</strong> experiment[8][9][10]. The HVS can
detect more easily <strong>the</strong> difference varying in low frequency
than high frequency. The CSF can be expressed by a
function and CSF graph is shown in Fig. 5.
Where,
1.
1
H( f ) 2.
6(
0.
192 0.
114 f ) exp( (
0.
114 f ) ) (2)
f f f is <strong>the</strong> frequency having unit of
2
x
2
y
cycle/degree. HVS have best sensitivity around
8[cycles/degree] and above 40[cycles/degree] its value
become smaller and smaller.
CSF Value
1.4
1.2
1
0.8
0.6
0.4
0.2
0
CSF Graph
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60
Frequency[Cycle/Degree]
Fig.5. The contrast sensitivity function graph.
There have been many ef<strong>for</strong>ts to use CSF to <strong>the</strong>ir
applications, especially in compression. In order to use <strong>the</strong>
CSF in real application relative sensitivity value is
important[10]. Without loss of generality, <strong>the</strong> maximum
CSF frequency can be normalized as 2 in DCT domain.
The DCT has good energy compaction property and it has
also fast algorithm. The DCT has no phase in<strong>for</strong>mation, it
only consider <strong>the</strong> magnitude of trans<strong>for</strong>med data. The CSF
has only magnitude, so it will be enough to consider <strong>the</strong>
proposed ACSF in DCT domain. The proposed adapted CSF
in DCT is ma<strong>the</strong>matically expressed by <strong>the</strong> followings.