用波动粒子二象性提升暗光图像增强的可解释性
Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement
- 将图像建模为概率波函数，引入物理不确定性机制
- 在低光照下显著降低亮度偏差，提升抗噪能力
- 适合关注可解释AI与物理启发模型的研究者
本研究对近期提出的数据相对论不确定性（DRU）框架进行了理论拓展，提出了一种从物理到人工智能的范式。通过将图像建模为概率波函数而非确定性状态，该范式明确整合了波动-粒子二象性，阐明了DRU如何利用光的内在物理不确定性来实现图像增强。这一方法提供了严格的可解释人工智能（XAI）框架，提升了对DRU如何缓解光照偏差并保持对数据噪声鲁棒性的理解。
原文摘要 · Abstract (English)
This study provides a theoretical expansion of the recent Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for image enhancement. By modeling images as probabilistic wave functions rather than deterministic states, the paradigm explicitly integrates wave-particle duality to illustrate the system flow of how DRU leverages the intrinsic physical uncertainty of light, a dimension requiring further theoretical discussion. Consequently, this paradigm provides a rigorous Explainable AI (XAI) approach that enhances the interpretability of how DRU mitigates illumination bias and maintains robustness against data noise.
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