DnSAD: Defocus and Spatially Adaptive Deblurring for Mura Compensation in Display Panels

초록

Mura artifacts, characterized by spatially non-uniform brightness or color inconsistencies, are critical defects in high-resolution display panels. Although in-focus images are ideal for camera-based mura compensation, defocused images are commonly captured in practical inspection environments to suppress moir & eacute; artifacts. However, defocus blur combined with optical distortion introduces spatially varying blur, which complicates accurate compensation. In this paper, we propose a defocus and spatially adaptive deblurring (DnSAD) framework for mura compensation in display panels. DnSAD addresses this challenge by capturing an additional dot-pattern image that provides essential information about spatially varying point spread functions (PSFs). The proposed framework is built upon a deep Wiener deconvolution backbone, extended to support multiple PSFs. The spatial location of each PSF is encoded into the model via a spatial attention module, enabling localized restoration tailored to the spatial blur distribution. Furthermore, a multi-scale refinement strategy is employed to progressively reconstruct high-frequency details. Experimental results on both real and synthetic datasets demonstrate that DnSAD effectively restores fine-grained mura patterns while adapting to spatial blur variations, outperforming both conventional and recent learning-based approaches.

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