Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement
Yiquan Gao
eess.IV cs.CV cs.LG math.OC quant-ph
Abstract
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.