Quantum-inspired vision model uses wave-particle duality for low-light enhancement

A new paper from Yiquan Gao, published on arXiv, presents a quantum-inspired vision framework that treats images as probabilistic wave functions rather than deterministic pixel arrays. This approach formally extends the recent Data Relativistic Uncertainty (DRU) framework by integrating wave-particle duality—a core concept from quantum physics—into the image enhancement pipeline. The paradigm is designed specifically for low-illumination conditions, where traditional deterministic methods often fail due to noise and bias. By leveraging intrinsic physical uncertainty of light, the model achieves better robustness against data noise and illumination bias, all while maintaining a rigorous Explainable AI (XAI) layer that makes the system's reasoning transparent.

The work sits at the intersection of image processing, computer vision, machine learning, optimization, and quantum physics. While the paper is primarily theoretical, it provides a foundational paradigm that could inspire practical implementations for low-light photography, night-vision systems, and medical imaging. The DRU framework's ability to explicitly model uncertainty opens the door for more interpretable and reliable AI systems in critical applications. As the field moves toward physics-informed machine learning, this quantum-inspired approach may become a key reference for future research.