Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement
For researchers in low-level vision and explainable AI, this work offers a theoretical framework for integrating quantum-inspired concepts into image enhancement, but it remains conceptual without empirical validation.
This paper extends the Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for low-illumination image enhancement, modeling images as probabilistic wave functions to improve interpretability and robustness against noise. No concrete performance numbers are provided.
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.