Technical Specialist Japan Display Inc. Mobara-shi, Chiba, Japan
This paper discusses the automatic detection of mura, which has been a long-standing challenge in the display industries. Using a dataset of 8000 images of OLED displays including four different types of mura, we found that a CNN having four sets of convolution and max-pooling layers can detect mura with the accuracy more than 0.8. To improve detection of low contrast mura, we employed contrast-enhancement and subspace-method, and the CNN accuracy improved to 0.868, close to the human visible test. The implementation of an automatic in-line mura-detection system is also discussed.