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<p>Microscopic bubbles within porcelain glazes encapsulate critical parameters of ancient ceramic production techniques. These bubbles are small, low-contrast, and poorly defined against complex backgrounds, making detail preservation and noise suppression challenging for existing methods. To address these limitations, this study employs a three-branch PBDWNet to extract the intrinsic structural information of bubbles. In the structure-preserving pathway, deformable feature awareness integrates deformable convolution with channel attention to adaptively capture bubble morphology and enhance multi-scale feature interaction. The detail enhancement branch applies wavelet feature fusion to decompose images into high- and low-frequency components, strengthening edge textures while suppressing background noise. The backbone network achieves unified representation of global and local information through cross-scale feature fusion. Experiments on the PRMI dataset show that PBDWNet outperforms mainstream segmentation models, surpassing the second best by 0.79% in bubble IoU and achieving the highest mean F1-score of 91.70%, demonstrating superior accuracy in complex scenarios.</p>

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PBDWNet: a multi-scale porcelain surface bubble segmentation network based on frequency-domain fusion and deformable feature awareness

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Jinwen Xing,
  • Ruichao Zhang,
  • Yangyang Liu,
  • Jingfan Xu,
  • Guohua Geng,
  • Pengbo Zhou,
  • Qi Zhang

摘要

Microscopic bubbles within porcelain glazes encapsulate critical parameters of ancient ceramic production techniques. These bubbles are small, low-contrast, and poorly defined against complex backgrounds, making detail preservation and noise suppression challenging for existing methods. To address these limitations, this study employs a three-branch PBDWNet to extract the intrinsic structural information of bubbles. In the structure-preserving pathway, deformable feature awareness integrates deformable convolution with channel attention to adaptively capture bubble morphology and enhance multi-scale feature interaction. The detail enhancement branch applies wavelet feature fusion to decompose images into high- and low-frequency components, strengthening edge textures while suppressing background noise. The backbone network achieves unified representation of global and local information through cross-scale feature fusion. Experiments on the PRMI dataset show that PBDWNet outperforms mainstream segmentation models, surpassing the second best by 0.79% in bubble IoU and achieving the highest mean F1-score of 91.70%, demonstrating superior accuracy in complex scenarios.