Image Demoireing via U-Net for Detection of Display Defects

Abstract

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.

Enhancement of GUI Display Error Detection Using Improved Faster R-CNN and Multi-Scale Attention Mechanism

“…Additionally, Guo et al presented iFixDataloss, a tool that automatically detects and fixes data loss issues in Android apps, including those caused by screen rotation. iFixDataloss outperformed existing techniques in terms of the number of detected issues and the quality of generated patches [17]. Display errors such as text overlap and empty values can negatively impact app usability and user experience.…”

Deep Learning for Screen-Shot Image Demoiréing: A Survey

“…To solve the problem of limited moiré datasets containing Mura defects, Kim et al [72] proposed to use a smartphone to display images with Mura defects and fixed the camera to collect moiré images containing Mura defects by moving or rotating the phone, and by inserting ArUco markers to align the Mura defect image and the corresponding moiré image. Besides, Kim et al [72] used U-Net as a baseline model and combined it with Fourier transform to calculate the frequency domain loss between images to ensure the removal of moiré, which significantly improved the PSNR of the images.…”