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<p>Microscopic imaging provides essential visual evidence for pathogen monitoring, but its shallow depth of field and the three-dimensional height variation of spores lead to pronounced defocus blur and structural degradation. Restoring such images is therefore crucial for reliable spore identification and downstream analysis. However, microscopic defocus is a spatially varying process that severely suppresses high-frequency structures, causing natural-image deblurring models to generalize poorly. In addition, optical constraints of microscopy make realistic sharp-blur pairs difficult to obtain, further limiting learning-based restoration. To address these challenges, we propose MicroDeblurNet, a single-image deblurring network specifically designed for microscopic defocus restoration. The model incorporates a convolutional block attention module to enhance spatial selectivity toward key pathogen structures, and employs depthwise over-parameterized convolutions to capture locally varying blur patterns more effectively, enabling spatially consistent and structurally coherent restoration. Furthermore, a spatial-frequency consistency loss is proposed to strengthen high-frequency detail recovery while maintaining color fidelity and morphological integrity. To support high-fidelity supervision, we propose a paired-data construction strategy based on Laplacian-pyramid fusion and construct a clear-blur microscopic dataset for cucumber downy mildew. The restored outputs of MicroDeblurNet are further applied to sporangia detection and semantic segmentation to evaluate their impact on high-level visual tasks. Finally, we build an integrated microscopic analysis platform that delivers standardized high-quality data and automated pathogen-structure recognition and analysis to support disease assessment and management. Experimental results demonstrate that MicroDeblurNet achieves an optimal balance across pixel-level, structure-level, and perception-level metrics, reaching a PSNR of 42.48 dB and SSIM of 0.9839, outperforming advanced state-of-the-art methods. In downstream tasks, MicroDeblurNet delivers higher detection recall and segmentation accuracy in challenging scenarios involving sporangia adhesion and background impurities, demonstrating its ability to enhance target discernibility, preserve structural completeness, and improve robustness under complex microscopic conditions.</p>

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MicroDeblurNet: high-fidelity restoration of spatially variant defocus in microscopic images for cucumber downy mildew

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Yiding Zhang,
  • Bo Wang,
  • Yuzhaobi Song,
  • Zonghuan Han,
  • Xiaoshuan Zhang,
  • Lingxian Zhang

摘要

Microscopic imaging provides essential visual evidence for pathogen monitoring, but its shallow depth of field and the three-dimensional height variation of spores lead to pronounced defocus blur and structural degradation. Restoring such images is therefore crucial for reliable spore identification and downstream analysis. However, microscopic defocus is a spatially varying process that severely suppresses high-frequency structures, causing natural-image deblurring models to generalize poorly. In addition, optical constraints of microscopy make realistic sharp-blur pairs difficult to obtain, further limiting learning-based restoration. To address these challenges, we propose MicroDeblurNet, a single-image deblurring network specifically designed for microscopic defocus restoration. The model incorporates a convolutional block attention module to enhance spatial selectivity toward key pathogen structures, and employs depthwise over-parameterized convolutions to capture locally varying blur patterns more effectively, enabling spatially consistent and structurally coherent restoration. Furthermore, a spatial-frequency consistency loss is proposed to strengthen high-frequency detail recovery while maintaining color fidelity and morphological integrity. To support high-fidelity supervision, we propose a paired-data construction strategy based on Laplacian-pyramid fusion and construct a clear-blur microscopic dataset for cucumber downy mildew. The restored outputs of MicroDeblurNet are further applied to sporangia detection and semantic segmentation to evaluate their impact on high-level visual tasks. Finally, we build an integrated microscopic analysis platform that delivers standardized high-quality data and automated pathogen-structure recognition and analysis to support disease assessment and management. Experimental results demonstrate that MicroDeblurNet achieves an optimal balance across pixel-level, structure-level, and perception-level metrics, reaching a PSNR of 42.48 dB and SSIM of 0.9839, outperforming advanced state-of-the-art methods. In downstream tasks, MicroDeblurNet delivers higher detection recall and segmentation accuracy in challenging scenarios involving sporangia adhesion and background impurities, demonstrating its ability to enhance target discernibility, preserve structural completeness, and improve robustness under complex microscopic conditions.