用极简方法实现顶尖指纹增强效果，兼顾清晰度与效率
Unleashing the Power of Simplicity: A Minimalist Strategy for State-of-the-Art Fingerprint Enhancement
- 采用上下文滤波与学习型方法，以简单架构提升图像质量
- 在挑战性数据库上优于复杂现有方法，显著减少噪声并提升细节
- 代码开源，适合安全验证、公安刑侦等需高效增强的场景
指纹识别系统依赖于人体指纹的独特特征，在现代安全与身份验证中至关重要。准确提取纹线细节（minutiae）是关键步骤，其性能高度依赖指纹图像质量。尽管近年来指纹增强技术不断进步，但现有先进方法仍难以应对低质指纹，且计算开销大。本文提出一种极简主义增强策略，引入两种新方法：上下文滤波法与基于学习的方法。这些方法在保持简洁的同时，持续优于复杂的主流方法，生成更清晰、准确且噪声更少的图像。在具有挑战性的潜指纹数据库上验证了其有效性。开源实现不仅保障可复现性，也推动领域进一步发展。研究强调了简单性在实现高质量增强中的价值，建议未来研究应在复杂性与实际效益间取得平衡。
原文摘要 · Abstract (English)
Fingerprint recognition systems, which rely on the unique characteristics of human fingerprints, are essential in modern security and verification applications. Accurate minutiae extraction, a critical step in these systems, depends on the quality of fingerprint images. Despite recent improvements in fingerprint enhancement techniques, state-of-the-art methods often struggle with low-quality fingerprints and can be computationally demanding. This paper presents a minimalist approach to fingerprint enhancement, prioritizing simplicity and effectiveness. Two novel methods are introduced: a contextual filtering method and a learning-based method. These techniques consistently outperform complex state-of-the-art methods, producing clearer, more accurate, and less noisy images. The effectiveness of these methods is validated using a challenging latent fingerprint database. The open-source implementation of these techniques not only fosters reproducibility but also encourages further advancements in the field. The findings underscore the importance of simplicity in achieving high-quality fingerprint enhancement and suggest that future research should balance complexity and practical benefits.
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