用幂函数建模图像明暗二元性，提升低对比度图像信息提取能力。
Modeling Image Tone Dichotomy with the Power Function
- 基于幂函数构建明暗二元性数学模型，揭示光照特性本质。
- 在低对比度图像中成功提取丰富视觉信息，优于现有增强方法。
- 适合图像增强、计算机视觉等需处理复杂光照场景的研究者。
本文提出基于幂函数的图像光照二元性建模方法，通过分析幂函数的数学特性，识别现有模型的局限性，并构建新模型以抽象表达光照的二元特征。该模型方程简洁，为经典与现代图像分析与处理开辟新路径。文章通过实际图像示例说明新模型如何有效管理图像感知中的二元性问题，证明了二元性图像空间在弱对比度条件下仍能提取丰富信息，尤其在色调、明度与色彩感知方面表现突出。与当前先进图像增强方法的对比验证了该方法的有效性。
原文摘要 · Abstract (English)
The primary purpose of this paper is to present the concept of dichotomy in image illumination modeling based on the power function. In particular, we review several mathematical properties of the power function to identify the limitations and propose a new mathematical model capable of abstracting illumination dichotomy. The simplicity of the equation opens new avenues for classical and modern image analysis and processing. The article provides practical and illustrative image examples to explain how the new model manages dichotomy in image perception. The article shows dichotomy image space as a viable way to extract rich information from images despite poor contrast linked to tone, lightness, and color perception. Moreover, a comparison with state-of-the-art methods in image enhancement provides evidence of the method's value.
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