DPFR-LLIE: Dual-Prior Frequency Routing for UAV Low-Light Image Enhancement

UAV images captured from high altitudes and long viewing distances often suffer from uneven illumination, low signal-to-noise ratios, color distortion, and platform motion. These coupled degradations suppress object boundaries and textures, leaving the degraded observation with insufficient evidence to reliably recover the structural details of small and distant targets. Existing low-light enhancement methods infer restoration cues mainly from this incomplete observation, therefore weak structural information can be mistaken for noise or blur and smoothed away. To obtain structural guidance that generalizes across low-light degradations, we analyze frozen DINOv3 feature representations learned from large-scale visual pretraining. We find that they retain cross-exposure structural correspondence under severe degradation, with low-, mid-, and high-frequency components showing their strongest correspondence at different feature depths. Based on this feature-depth–frequency dependency, we propose Dual-Prior Frequency Routing for Low-Light Image Enhancement (DPFR-LLIE). We first design a frequency-specific feature-depth aggregation module to learn band-specific mixtures of frozen DINOv3 features. Then we design a bounded input-conditioned routing module to allocate and inject the resulting structural guidance with controlled magnitude. Finally, we propose a photometric conditioning module to modulate encoder features with image-derived luminance and chrominance cues. Across the UAV scenes, DPFR-LLIE achieves the best quantitative scores and the strongest qualitative results among the evaluated methods. Specifically, compared with the current SOTA method StarIR, DPFR-LLIE improves PSNR/SSIM by 0.71 dB/0.014 on Synthetic VisDrone Low-Light and by 1.42 dB/0.022 on LOL-Blur. With the same frozen detector, the enhanced UAV images further improve AP and ${\mathrm{AP}}_S$ by 0.27 and 0.31 points. The code will be released upon acceptance.