{"ID":5675141,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-05T05:29:26.995498422Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01731","arxiv_id":"2607.01731","title":"Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement","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.","short_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 illustra...","url_abs":"https://arxiv.org/abs/2607.01731","url_pdf":"https://arxiv.org/pdf/2607.01731v1","authors":"[\"Yiquan Gao\"]","published":"2026-07-02T05:38:55Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"cs.LG\",\"math.OC\",\"quant-ph\"]","methods":"[]","has_code":false}