提出可扩展到多类的归一化潜力对比度，用于评估古籍图像质量。
Potential Contrast: Properties, Equivalences, and Generalization to Multiple Classes
- 引入归一化版本潜力对比度，消除图像格式依赖。
- 证明等式实现两分类向多分类及连续场景推广。
- 在中世纪乐谱透墨图像上验证方法有效性，适合文化遗产分析。
潜力对比度通常用作图像质量度量，量化在任意灰度变换后，图像中两类像素样本间可能达到的最大对比度。它已被应用于文化遗产领域，仅需少量标注像素即可评估多光谱图像。本文提出一种归一化版本的潜力对比度，消除了对图像格式的依赖，并证明了使该方法可推广至多于两类以及连续设置的等式关系。最后，通过一个包含页面背面渗色的中世纪音乐手稿实例，展示了多类别归一化潜力对比度的实用性。代码实现已公开于 https://github.com/wallacepeaslee/Multiple-Class-Normalized-Potential-Contrast，涵盖原始算法与新等式推导的多类推广。
原文摘要 · Abstract (English)
Potential contrast is typically used as an image quality measure and quantifies the maximal possible contrast between samples from two classes of pixels in an image after an arbitrary grayscale transformation. It has been applied in cultural heritage to evaluate multispectral images using a small number of labeled pixels. In this work, we introduce a normalized version of potential contrast that removes dependence on image format and also prove equalities that enable generalization to more than two classes and to continuous settings. Finally, we exemplify the utility of multi-class normalized potential contrast through an application to a medieval music manuscript with visible bleedthrough from the back of the page. We share our implementations, based on both original algorithms and our new equalities, including generalization to multiple classes, at https://github.com/wallacepeaslee/Multiple-Class-Normalized-Potential-Contrast.
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