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

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 me...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2023-09, Vol.33 (9), p.1-1
Main Authors: Min, Sung-Jun, Kong, Kyeongbo, Kang, Suk-Ju
Format: Article
Language:English
Subjects:
Cameras
Frequency analysis
Frequency domain analysis
Image acquisition
Image contrast
image deblurring
Image degradation
Image edge detection
Image quality
Image restoration
Inspection
Kernel
Kernels
Moire patterns
out-of-focus image
Task analysis
Training
Vision inspection
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Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2023.3241931