针对指纹图像不同区域设计差异化增强策略，提升低质量区域恢复效果。
A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae
- 构建三分支网络，分区域采用定制化增强方法
- 在MOLF和MUST数据集上优于现有算法
- 融合方向场与特征点信息，增强模型泛化能力
潜在指纹增强是潜在指纹识别中的关键步骤。现有的基于深度学习的增强方法仍难以满足实际应用需求，尤其在恢复低质量指纹区域方面表现不足。鉴于潜在指纹不同区域需要不同的增强策略，本文提出三分支空间融合网络（TBSFNet），通过分区域采用定制化策略实现图像增强。为进一步提升网络泛化能力，将方向场与特征点相关模块融入TBSFNet，引入多层级特征引导网络（MLFGNet）。在MOLF和MUST数据集上的实验结果表明，MLFGNet显著优于现有增强算法。
原文摘要 · Abstract (English)
Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancement strategies, we propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies. Furthermore, to improve the generalization capability of the network, we integrate orientation field and minutiae-related modules into TBSFNet and introduce a Multi-Level Feature Guidance Network (MLFGNet). Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms.
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