## 5 Conclusions  
The supplementary results in Appendices A and B provide additional theoretical foundations and experimental evidence supporting our approach. Appendix A presents detailed mathematical proofs for Theorems A.1 and A.2, establishing the theoretical groundwork for our proposed frequency gradient alignment principle and SCCA optimality. Appendix B showcases extensive visual comparisons across multiple datasets and lighting conditions, as well as the algorithmic pseudocode for the corresponding modules, confirming UMFNet's consistent superiority in preserving high-frequency details while effectively suppressing noise in challenging low-light environments. In this work, we present UMFNet, a frequency-aware architecture that resolves the spectral trilemma in low-light object detection. Our research presents three key breakthroughs. Multi-domain spectral  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Sensor** & **Lux Range** & **Noise Model** & **Test Samples** \\ \hline ExDark (Base) & Smartphone & 0.1–15 & Gaussian+Poisson & 1472 \\ DARK FACE & Surveillance Cam & 0.05–5 & Motion Blur & 3108 \\ LOL & Digital Camera & 0.5–10 & Shot Noise & 100 \\ FLIR ADAS (IR) & Automotive Thermal & – & Non-Uniformity & 874 \\ BDD100K-night & Vehicle Dashboard & 1–50 & Mixed Urban Noise & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-dataset evaluation configuration.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **ExDark** & **DarkFace** & **LOL** & **FLIR ADAS** & **BDD100K-Night** & **Avg. GDR \(\downarrow\)** \\ \hline YOLOv8 & 68.7 & 51.2 & 58.3 & 32.7 & 63.5 & 27.4\% \\ RT-DETR & 70.7 & 55.4 & 61.2 & 36.8 & 66.3 & 22.1\% \\ GLARE & 71.6 & 56.3 & 63.1 & 37.9 & 67.5 & 20.9\% \\ YOLOv9 & 71.2 & 55.8 & 62.7 & 37.2 & 67.1 & 21.5\% \\ EfficientDet-B7 & 69.8 & 54.1 & 60.8 & 35.6 & 65.3 & 24.2\% \\ \hline UMFNet (Ours) & 73.1 & 58.7 & 65.4 & 40.2 & 69.8 & **17.3\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Zero-shot cross-domain mAP@50 comparison (%, 2023–2024 methods).  
synchronization: The proposed frequency-adaptive fusion (FAF) module achieves a 73.1% mAP@50 on EXDark, 6.4% higher than the YOLOv8 baseline, leading in 9/12 object classes. In photon-starved conditions (0.1 lux), FAF's learnable wavelet decomposition has 2.4\(\times\) higher photon utilization efficiency than fixed-filter approaches. Dual-domain attention coordination: Through ablation studies, it is shown that our spatial-channel coordinated attention (SCCA) mechanism is crucial. It reduces high-frequency hallucinations by 37% via entropy-controlled gating and improves the LF component alignment precision by 19.7% under motion blur. Physics-grounded optimization: The spectral perception loss bridges computational photography and deep detection, validated by a 9.3% recall gain for pedestrians at 5 lux (DarkFace benchmark), 42% higher robustness against adversarial noise (EMVA 1288 tests), and a 69.8% mAP@50 under thermal-IR domain shift. This work establishes a dual theoretical-practical foundation for frequency-aware vision systems. We will publish all models and benchmarks in open-source format to promote community research in low-light perception.