Out-of-Focus Image Deblurring for Mobile Display Vision Inspection

Abstract

In vision inspection tasks, moiré patterns caused by frequency aliasing can severely degrade image quality. To prevent moiré patterns, we used images that were intentionally out-of-focused, and we performed deblurring to restore details during the acquisition of the images. As existing deblurring methods fail to output satisfactory results for low-contrast Mura images, we applied some simple techniques, minimum-maximum normalization, and edge mask fine-tuning to one of the state-of-the-art non-blind deblurring methods by utilizing parametric generalized Gaussian kernels. Structural image details were preserved through edge mask fine-tuning, and image contrast was improved with minimum-maximum normalization. By parameterizing the blur kernel as a generalized Gaussian kernel, we greatly improved the robustness of the blind image deblurring. We evaluated the effects of each module by conducting thorough experiments. The proposed method showed better performance than existing blind deblurring methods for blur-specific no-reference metrics, the image profile, and frequency domain analysis.