Abstract
Image quality acts as a major factor in determining its performance in a vision inspection task. The moire pattern caused by frequency aliasing severely degrades the visual quality in display devices, where such high‐quality images are required. To remove these quality undermining patterns, the images are acquired by intentional defocusing. Then, to restore the details lost during the image acquisition, image deblurring is used. The existing deblurring methods fail to output satisfactory results for low contrast Mura images. To solve this problem, we present a novel approach using a generalized Gaussian kernel for real‐world vision inspection tasks. We evaluated the performance and experimented under different settings to validate the robustness of the proposed method. The performance for the proposed method has improved in no‐reference image quality assessment metrics.
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