In this paper, we propose an automatic inspection system, which can automatically detect four types of muras on an LCD panel: cluster mura, v-band mura, rubbing mura, and light leakage mura. To detect cluster muras, the Laplacian of Gaussian (LOG) filter is used. A multi-resolution approach is proposed to detect cluster muras of different scales. To speed up the processing speed, this multi-resolution approach is actually implemented in the frequency domain. To detect v-band muras, we check the variation tendency of the projected 1-D intensity profile. Then, v-band muras are detected by identifying these portions of the 1-D profile where a large deviation occurs. To detect rubbing muras, we designed a frequency mask to detect distinct components in the frequency domain. To detect light leak muras, we apply image mirroring over the boundary parts and adopt the same LOG filter that has been used in detecting cluster muras. All four types of mura detection are integrated together in an efficient way and simulation results demonstrate that this system is indeed very helpful in detecting mura defects.
In this paper, we suggest to use a 2-D LOG filter to inspect Cluster Mura defects on the FOS images of LCDs, either for round-type Cluster Mura defects or rectangular-type Cluster Mura defects. With the 2-D LOG filter, the optimal threshold is analyzed with the SEMU formula. Also, we propose a curvature test approach to detect V-Band Mura defects. In the curvature test approach, a 1-D LOG filter is used to achieve the curve with the smooth curvature tendency. With this estimated curve, V-Band Mura defects could be detected easily. The FOS surface reconstruction verifies this detection approach in a reasonable way. Either for the Cluster Mura detection approach or for the V-Band Mura detection approach, the simulation results demonstrate the LOG filters is very useful in the development of detection algorithms for automatic optical inspection of Mura-like defects.