提升掌纹图像对比度，减少块状伪影。
ILACS-LGOT: A Multi-Layer Contrast Enhancement Approach for Palm-Vein Images
- 分层高斯加权重叠块自适应增强对比度。
- 在多个数据集上优于现有方法，显著降低块状伪影。
- 适合掌纹识别与生物特征增强场景。
本文基于前期工作，提出改进的多层对比度增强方法ILACS-LGOT（Intensity-Limited Adaptive Contrast Stretching with Layered Gaussian-weighted Overlapping Tiles），取代原多重重叠块（MOT）方法。新命名更准确反映其对比度增强与块状伪影抑制机制。本文提供更详尽分析，包括扩展评估、可视化结果和样本对比，验证了该方法在多个数据集上的有效性，显著优于现有技术。
原文摘要 · Abstract (English)
This article presents an extended author's version based on our previous work, where we introduced the Multiple Overlapping Tiles (MOT) method for palm vein image enhancement. To better reflect the specific operations involved, we rename MOT to ILACS-LGOT (Intensity-Limited Adaptive Contrast Stretching with Layered Gaussian-weighted Overlapping Tiles). This revised terminology more accurately represents the method's approach to contrast enhancement and blocky effect mitigation. Additionally, this article provides a more detailed analysis, including expanded evaluations, graphical representations, and sample-based comparisons, demonstrating the effectiveness of ILACS-LGOT over existing methods.
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