Image Demoireing via U-Net for Detection of Display Defects

Mura defects, which occur during display manufacturing, degrade the quality of the display. Therefore, Mura detection is critical. When the camera is focused on the display for accurately detecting Mura defects, a moire pattern occurs in a captured image because of the frequency difference between the subpixels of the display and the color filter array of the camera. Typical image data handled with existing demoireing methods do not have Mura defects and include synthetic moire images. Therefore, we created a dataset to detect Mura defects that include real moire patterns, classified into two categories: weak and strong. We propose a new demoireing framework to remove the moire patterns in the captured image, thereby accurately detecting Mura defects. We also propose inserting ArUco markers for accurate alignment and automation, conducting multiple experiments with U-Net. Based on the captured data, the proposed U-Net, which combines a frequency loss and data augmentation, improves the performance by 6.41 dB higher for the weak moire pattern and 4.14dB higher for the strong moire pattern than state-of-the-art networks in terms of peak signal-to-noise ratio.